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THE CORRELATION BETWEEN 
TEACHER CLARITY OF COMMUNICATION 
AND STUDENT ACHIEVEMENT GAIN: 
A META-ANALYSIS 



By 

FRANK FENDICK 



A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL 
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT 
OF THE REQUIREMENTS FOR THE DEGREE OF 
DOCTOR OF PHILOSOPHY 



UNIVERSITY OF FLORIDA 'UNIVERSITY OF fLOniDA imm 
1990 



ACKNOWLEDGMENTS 



It is my pleasure to acknowledge my debt for the 
guidance I have received over many years from my chairman. 
Dr. James Algina, and from the members of my committee, 
Dr. Wilson Guertin, Dr. Patricia Ashton, and Dr. Robert 
Ziller. Please accept my sincere thanks. 



- ii - 



TABLE OF CONTENTS 



Page 



ACKNOl^fLEDGMENTS ii 

LIST OF TABLES vi 

LIST OF FIGURES vii 

ABSTRACT viil 

CHAPTERS 

I INTRODUCTION 1 

Teacher Clarity of Communication 1 

Clarity of Speech 1 

Clarity of Organization 1 

Clarity of Explanation 2 

Clarity of Examples and Guided Practice 3 

Clarity of Assessment of Student Learning 3 

Summary of the Dimensions of Teacher Clarity ... 3 

The Problem Studied in This Dissertation 4 

The Rationale for the Study 4 

Practical Significance and Objectives of the 

Study 5 

Theoretical Significance of the Study 7 

Limitation of this Dissertation 9 

Definitions 10 

Definition of Teacher Clarity 10 

Definition of Student Achievement Gain 11 

Outline of the Dissertation 13 

II REVIEW OF THE LITERATURE 14 

Teacher Clarity Literature 14 

The Dimensions of Teacher Clarity 14 

Clarity of Speech 20 

Clarity of Organization 22 

Clarity of Explanation 23 

Examples and Guided Practice 24 

Assessment of Student Learning 25 

Analysis Literature 25 

General Problems of Meta-analysis 25 

Criteria for Evaluating Meta-analyses 28 

Criticisms of Meta-analyses in Education 29 



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Page 



III REVIEW OF METHODOLOGY 31 

Objective 31 

Uncertainty and Variance 32 

Summary of Procedure 32 

Defining the Problem 33 

Finding the Studies 35 

Describing, Classifying, and Coding Research 

Studies 36 

The Dimensions of Teacher Clarity 35 

Description and Classification 39 

Coding Nominal Values 41 

Technigues of Analysis 42 

Looking at the Data 42 

Problems in Accumulating the Effect Sizes From 

Each Study 43 

Glass, McGaw, and Smith (1981) 46 

Hunter, Schmidt, and Jackson (1982) 44 

Rationale for the Hunter and Schmidt Method .... 45 

Hedges and Olkin (1985) 48 

Hedges (1988) 50 

Summary of the Analyses Used in This Study 50 

IV RESULTS AI^J'D A.MALYSES 53 

Results 53 

Freguency Distribution 54 

Removal of Outliers 54 

Reliability of Dimensions of Teacher Clarity ... 58 

Characteristics of the Reduced Data Set 58 

Relationships Between Characteristics 61 

Glass Analysis 64 

Treating Each Effect Size as Independent 64 

Using Tukey's Jackknife Method 65 

Regression Eguations 66 

Hunter Analysis 71 

The Weighted Mean Effect Size From Each Study .. 71 

Regression Eguations 73 

Hedges Analysis 74 

The Weighted Mean Effect Size From Each Study .. 74 

Regression Equations 77 

Comparison of Results Using Different Methods of 

Analysis 78 

Confidence Intervals 78 

Regression Equations 79 

Analysis of Subsets 80 

Differences Due to Method of Analysis 88 

V CONCLUSIONS AND DISCUSSION 95 

Questions Answered in This Dissertation 95 

Discussion 100 



- iv - 



Page 

APPENDICES 

A STUDIES USED IN THE META-ANALYSIS 104 

B REJECTED STUDIES 117 

REFERENCES 122 

BIOGRAPHICAL SKETCH 137 



- V - 



LIST OF TABLES 

Page 



3-1 Teacher Behaviors Defining the Dimensions of 

Teacher Clarity 34 

3- 2 Coding the Dimensions of Teacher Clarity 42 

4- 1 Characteristics of Studies Producing Low Outliers . 56 

4-2 Characteristics of Studies Producing High Outliers 58 

4-3 Characteristics of Reduced Data Set 59 

4-4 Correlations in Data Set 62 

4-5 Tukey's Jackknife Method for Determining the 

Confidence Interval of the Effect Sizes 67 

4-6 Confidence Intervals of All Effect Sizes 

Using Different Methods 78 



4-7 Regression Equation Results Using Different Methods 79 

4-8 Confidence Intervals of Subsets of Effect Sizes ... 81 

4-9 Differences in the Last Digit in the Results in 
Table 4-8 From Average Corrected Confidence 
Intervals Using the Four Methods of Analysis .... 89 

4-10 Summary of Differences From Mean Corrected 

Confidence Intervals Using the Four Methods 



of Analysis 94 

A-1 Teaching Behaviors and Assumed Dimensions of 

Teacher Clarity 104 

A-2 Characteristics and Results of Studies 113 

B-1 Rejected Studies and Reason for Rejection 117 



- vi - 



LIST OF FIGURES 

Page 

1-1 Factors Affecting the Rate of Transmission of 

Correct Information 8 

1-2 Some Factors Affecting the Rate of 

Achievement Gain of the Students 9 

4-1 Frequency Distribution of the Effect Sizes 55 



- vii - 



Abstract of Dissertation Presented to the Graduate School of 
the University of Florida in Partial Fulfillment of the 
Requirements for the Degree of Doctor of Philosophy 



THE CORRELATION BETWEEN 
TEACHER CLARITY OF COMMUNICATION 
AND STUDENT ACHIEVEMENT GAIN: 
A META-ANALYSIS 

By 

FRANK FENDICK 

August 1990 

Chairman: Dr. James Algina 

Major Department: Foundations of Education 

The problem was to determine the correlation between 
teacher clarity and the mean class student learning 
(achievement gain) in normal public-education classes in 
English-speaking, industrialized countries. The grade range 
was assxamed to be from Grade 1 through undergraduate study. 
A normal class was defined as one in which the students are 
not special in any way and the class runs for a minimum of 6 
weeks. Class achievement gain was defined as the mean 
posttest score minus the mean pretest score on a valid 
(relevant), reliable test of the subject matter that was 
taught on the course. Teacher clarity was defined as 
clarity of (a) organization, (b) explanation, (c) examples 



• • • 

- viii - 



and guided practice, and (d) assessment of student learning. 
Clarity of speech was regarded as a prerequisite of teacher 
clarity. Student achievement gain was defined as the class 
posttest score minus the pretest score or its equivalent. 
Different methods of meta-analysis were used in order to 
determine whether they resulted in significantly different 
results . 

It is of practical and theoretical importance to know 
the relationship between class learning and teacher clarity. 
It is also important to know how the measured relationship 
varies with the context of the learning and with the method 
of analysis. 

The correlation between teacher clarity and student 
achievement gain (effect size) was found to be .35 +/- .05. 
The method of meta-analysis used made no difference. The 
different dimensions of teacher clarity did not produce 
significantly different effect sizes. A factor score 
combining at least two dimensions of teacher clarity had a 
significantly higher effect size: .60 +/- .13. 

Larger effect sizes were obtained with (a) student 
raters rather than observers, (b) experienced rather than 
inexperienced teachers, and (c) college rather than 
elementary school teachers. Class size and subject taught 
made no difference to the effect size. 



ix - 



CHAPTER I 
INTRODUCTION 

A tale should be judicious, clear, succinct; 
The language plain, the incidents well link'd; 
Tell not as new what ev'ry body knows; 
And, new or old, still hasten to a close. 

William Cowper, 1731-1800 

The topic of this dissertation is the correlation 
between teacher clarity of communication and the achievement 
gain of the students. This was estimated by a meta-analysis 
of all available studies that could be located. 

Teacher Clarity of Communication 
Clarity of Speech 

Communication between teacher and student cannot occur 
if the student cannot hear or understand what the teacher is 
saying. Thus teacher ability to speak loudly enough and in 
a manner such that the students can comprehend the teacher's 
speech is a necessary, but not sufficient, condition for 
communication to occur. Clarity of speech (SP) is regarded 
as a prerequisite of teacher clarity of communication. 
Clarity of Organization 

It is assumed in this dissertation that the teacher's 
task is to assist as many as possible of her or his students 
to pass an examination at the conclusion of the course (with 
as high a score as possible). The teacher's first task is 
to determine the end points of the course and the ground 

- 1 - 



- 2 - 

that must be covered: The teacher must determine where the 
students are at the beginning and exactly what they must be 
able to do at the end. She or he must then plan to cover 
the necessary work to be accomplished in the time available. 
That is, the teacher must give a pretest (oral or written) 
that reviews the prerequisites for the course and any topics 
on the course that the students might already know. The 
teacher must study past forms of the examination paper in 
order to determine what the students must do in the 
posttest, and must then schedule the work to be covered/ 
allowing time for review and test practice. This 
organization of teaching time can be summed up as (a) 
determining and stating the objectives of the course, (b) 
covering the topics that are required by the posttest, and 
(c) reviewing what has been covered. This organization of 
teaching time has to take place at the level of the 
individual topic or lesson as well as for the course as a 
whole. This is clarity of organization (ORG). 
Clarity of Explanation 

The teacher must explain the subject matter of the 
lesson in such a way that it is easy for the students to 
understand. In order to do this the teacher must (a) 
explain things simply and make them interesting (otherwise 
the students will not listen), (b) repeat and stress 
directions and difficult points, (c) introduce new content 
in small steps and relate it to content that has been 
already mastered by the students, and (d) teach at a pace 



- 3 - 

appropriate to the topic and to the students. This is 

clarity of explanation (EXP). 

Clarity of Examples and Guided Practice 

The students will not be able to efficiently answer 
questions of the type that are on the posttest without 
practice in doing so. The teacher must (a) demonstrate 
examples of answering posttest-type questions, (b) answer 
any questions that the students might have, (c) give the 
students enough time to practice (in class, for homework, 
and on practice tests), (d) explain points that have not 
been answered well and provide standards and rules for 
satisfactory performance, and (e) provide the students with 
knowledge on how well they are progressing toward scoring 
well on the posttest. This is clarity of examples and 
guided practice (EGP). 

Clarity of Assessment of Student Learning 

The teacher cannot communicate well without receiving 
feedback from the students. The teacher checks whether the 
students are understanding by (a) asking questions during 
the presentation, (b) encouraging relevant discussion, and 
(c) checking the students' classwork, homework, and tests. 
This is clarity of assessment of student learning (clarity 
of feedback from student to teacher) (ASL). 
Summary of the Dimensions of Teacher Clarity 

The dimensions of teacher clarity are assumed to be 
clarity of (a) organization, (b) explanation, (c) examples 
and guided practice, and (d) assessment of student learning 



- 4 - 

(feedback from student to teacher). Clarity of speech is 
assumed to be a prerequisite of teacher clarity. 

The Problem Studied in This Dissertation 

What is the confidence interval of the correlation 
between teacher clarity of communication and the achievement 
gain of the students in the population covered by this 
study? The confidence interval of the correlation is the 
range of values that is estimated to have a 95% chance of 
including the true value. 

The population . The population of students and 
teachers assumed to be covered by this study is all classes 
in public institutions (Grade 1 though undergraduate) where 
the education is of the American (European) type euid the 
students or teachers are not selected as being in anyway 
exceptional. 

Extension of the problem . Does the confidence interval 
change with such factors as (a) the methods of meta-analysis 
used, (b) the dimension of teacher clarity, (c) grade level, 
(d) subject taught, (e) any other properties of the 
situation of teaching and learning, or (f) the analysis and 
reporting of the correlation? 

The Rationale for the Study 

The rationale for determining the confidence interval 
of the correlation between teacher clarity of communication 
and student achievement gain (and how it varies with various 
factors) rests on the practical use of such information and 
on the contribution that it can make to the theory of 



- 5 - 

teaching. The rationale for determining whether the 
confidence interval varies with the details of the type of 
meta-analysis used is to determine the simplest method that 
can produce a valid confidence interval. 
Practical Significance and Objectives of the Study 

If we know the correlation (r.) between teacher clarity 
and student achievement gain (student learning), we know the 
proportion of the variance in achievement gain that is 
accounted for by variance in teacher clarity (given by r} x 
100%). In order to determine the correlation between 
teacher clarity and student achievement gain, it is 
necessary to conduct a meta-analysis of the relevant 
studies. In a meta-analysis the researcher guantifies the 
results from a number of studies in the form of an average 
correlation coefficient so that the overall magnitude of the 
average result can be readily grasped (Gage, 1979). 

In this study I set out to answer the following 
questions : 

1. What is the strength of the relationship between 
teacher clarity and student learning? 

2. Do clarity of (a) organization of the lesson (and 
course), (b) explanation (and speech), (c) examples and 
guided practice, and (d) assessment of student learning have 
different relationships to student learning? 

3. Do student ratings of teacher clarity have a higher 
correlation with student learning than observer ratings? 



- 6 - 

4. Is teacher clarity more predictive of student 
learning in subjects based on student verbal ability or in 
those based on numerical ability? 

5. Is teacher clarity more predictive at college, at 
secondary school, or at elementary school? Does the accuracy 
of prediction vary with grade? 

6. Is teacher clarity more predictive in large classes? 

7. Does teacher clarity have a stronger relationship 
with student learning when the teacher is experienced than 
when she or he is inexperienced? 

8. Which factors present in the investigation of 
relationships between teacher clarity and student learning 
are likely to result in an inaccurate estimation of the 
correlation? 

9. Do the confidence intervals around the mean 
correlations obtained in these various circumstances vary 
significantly with the methods of analysis used? If they do, 
which method is likely to produce the most valid interval? 

If they do not, which is the easiest method? 

The answers to these guestions are clearly important for 
both the theory and practice of education. From a practical 
point of view, determining the correlations between teacher 
clarity and student learning in different circumstances can 
influence the amount of effort teachers are prepared to exert 
in order to achieve clarity. It should also influence the 
emphasis put on this topic by teacher educators. 



- 7 - 

From the research point of view, a finding that, for 
example, student rating of teacher clarity is more accurate 
than observer rating might encourage small groups of 
teachers to do their own studies of the relationship between 
teacher clarity (and similar variables) and student 
achievement in their own situations (grade level, subject 
area, and type of student). Gage and Needels (1989) stated 
"In just two of these settings — grade level and subject 
matter — the need for further process-product research is 
glaring" (p. 289). Future meta-analyses of hundreds of 
these small studies would greatly increase our knowledge of 
important relationships in classroom teaching. 
Theoretical Significance of the Study 

The relationship between communication variables in the 
classroom should be subject to an overall theory of 
communication in any setting. If this study relates the 
dimensions of teacher clarity to communication theory (also 
called information theory), and shows that (a) the different 
dimensions have significantly different correlations with 
achievement gain and (b) the teacher-behavior variables 
assumed to define the dimensions produce homogeneous sets of 
correlations, then the study will have helped to advance our 
understanding of communication in the classroom. 



- 8 - 



Communication theory . The basic assumptions of 
communication theory are illustrated in Figure 1-1. 



SOURCE 



Correction Data 
> 



OBSERVER 



TRANSMITTER 





RECEIVER 






— > — 


— >— 





CORRECTING 
DEVICE 



►0/P 



From "An introduction to information theory: Symbols, signals 
and noise" by J. R. Pierce, 1980. Copyright 1961 by J. R. 
Pierce. Adapted by permission. 

Figure 1-1 . Factors Affecting the Rate of Transmission 
of Correct Information 



The task is to transmit correct information from the 
source to the output (0/P) of the system at as fast a rate 
as possible. The rate of transmission depends on (a) the 
clarity (lack of noise or distortion) of the signal from the 
transmitter to the receiver, (b) the speed and accuracy with 
which the observer detects differences between the output of 
the source and the output of the receiver (detection of 
errors), and (c) the speed with which the correcting device 
removes errors. 

Communication theory in the classroom . Figure 1-2 
shows this model of communication adapted to classroom 
teaching. 



- 9 - 



ANSWERS 
TO TEST 
QUEST. 



Correction Data 



Examples 



TEACHER 
OBSERVER 



ASL 



TEACHER 




STUDENT 






GUIDED 


TRANSMITTER 




RECEIVER 




— >- 


PRACTICE 


EXP & ORG 


> 


->— 




EGP 



►0/P 



Figure 1-2 . Some Factors Affecting the Rate of Achievement Gain 
of the Students (0/P = output — student performance on posttest). 



The rate of transmission of correct information (rate 
of learning) depends on (a) the clarity of organization and 
explanation of the signal from the transmitter (teacher) to 
the receiver (student), (b) the speed and accuracy with 
which the observer (teacher) detects differences between the 
output of the source (good answers to test-like questions) 
and the output of the receiver (detection of errors by the 
student — assessment of student learning), and (c) the speed 
with which the correcting device (guided practice) removes 
errors . 

Limitation of This Dissertation 

This study can only answer the questions in the 
preceding section to the extent that the primary research 
has been done and the results are available in the 
literature. 



- 10 - 

Definitions 
Definition of Teacher Clarity 

Teacher Clarity is assumed to be a measure of the 
clarity of communication between teacher and students — in 
both directions. It is assumed to have four dimensions 
(plus a prerequisite — clarity of speech) : 

1. Clarity of organization . The teacher must give 
structure to the lesson (and course). She or he does this 
by (a) stating objectives and relating them to the course 
objectives (the topics on the posttest), (b) clearly 
relating the teaching to the objectives, and (c) reviewing 
what has been covered in the lesson (and course). 

2. Clarity of explanation . The teacher is clear about 
what he or she explaining and is good at getting the 
students to understand. 

3. Clarity of examples and guided practice — seatwork 
with the teacher helping as required. The teacher 
demonstrates on the board examples of the type the students 
are required to do for seatwork, homework, and tests. The 
teacher clearly explains as she or he goes through the 
example what is being done and why. The teacher gradually 
gets the students to do more of the work themselves until 
most can make quick and accurate progress without help. 

4. Clarity of assessment of student learning — feedback 
from student to teacher. The teacher cannot hope to achieve 
clear communication unless she or he studies the students' 
written, verbal, and nonverbal responses that indicate 
whether they have understood. 



- 11 

Clarity of speech . In addition to the preceding 
dimensions, clarity of speech is assumed to be a 
prerequisite of clarity of explanation. A low score on 
clarity of speech will necessarily indicate a low score on 
clarity of explanation; it does not follow, however, that a 
high score on clarity of speech indicates a high score on 
clarity of explanation (Cruickshank & Kennedy, 1986). 
Clarity of explanation is concerned with what the teacher i 
saying, providing the students can hear and comprehend the 
language used by the teacher to say it: The teacher speaks 
loudly enough so that everyone can hear, and her or his 
accent is not sufficiently different from that of the 
students to make communication difficult. The teacher 
speaks with expression and is not monotonous and dull. The 
teacher's speech is not made difficult to understand by the 
use of vague terras, mazes (false starts; see, e.g., Hiller, 
Fisher, £< Kaess, 1969), ambiguous pronouns (e.g., the 
teacher says "he" and the students have no idea to whom the 
teacher is referring), or continual "uh"s. 
Definition of Student Achievement Gain 

In an ideal study, the students would be randomly 
assigned to classes. On the first and last day of the 
course they would take a relevant, valid, and reliable test 
of the course objectives. The pretest, a measure of 
intelligence, present CPA (grade point average), and the 
reason why the student is taking the course (when optional) 
would be used to check that random assignment had resulted 
in there being no significant difference in the students in 



- 12 

each class. If this is found to be true, the class 
achievement gain is the class mean of the difference betve 
each student's posttest and pretest score (simple gain 
score). If there is a significant difference in the 
students, each student's posttest score is predicted from 
the pretest and precourse measures and the relative 
influence of the teachers is estimated from the mean for a 
the students in the class of the difference between a 
student's actual posttest score and her or his predicted 
score (residual gain score). 

In a real study, random assignment is seldom possible 
and the pretests and posttests used often do not match tht 
objectives of the course at the appropriate level (Porter, 
1905). In this study, I v;ill include all research studie; 
pertaining to the correlation between teacher clarity and 
class learning unless the posttest used in the study is nt 
a valid m^easure of the subject matter taught; otherwise ( 
the decision to include or not include a study is likely \ 
be subjective, (b) useful information will be discarded, and 
(c) there v/ill not be a sufficient number of studies to 
analyze. It will also be necessary to record the measure 
used to estimate achievement gain in order to test whethe 
this results in a difference in the correlation between 
teacher clarity and student learning. 

Student achievement is assumed to be defined by any 
achievement measure that (a) is taken by the students in 
the classes or sections being compared and (b) is given t 
classes where the students have not been selected by abil 



1 

- 13 - 

(or any variable related to ability) for the different 
classes unless a pretest is taken so that achievement gain 
(or its equivalent) can be measured directly. 

Outline of the Dissertation 
In chapter II/ I report and discuss the literature that 
supports the assumed dimensions of teacher clarity and the 
low-inference teacher behaviors that are assumed to define 
those dimensions. I also give (a) an account of some of the 
problems in conducting meta-analyses, (b) criteria for 
evaluating them, and (c) some criticisms on how 
meta-analyses have been used in education. In chapter III, 
I review the methodology for conducting meta-analyses of 
correlations according to each of three leading proponents 
(Glass, McGaw, & Smith, 1981; Hedges & Olkin, 1985 and 
Hedges, 1988; and Hunter, Schmidt, & Jackson, 1982), and 
detail the procedures that have been used in this analysis. 
The results are given in chapter IV and the discussion and 
conclusions comprise chapter V. 



aiAPTER II 
REVIEW OF THE LITERATURE 



Teacher Clarity Literature 
The Dimensions of Teacher Clarity 

I have assumed that the dimensions of teacher clarity 
are clarity of (a) organization (ORG), (b) explanation (EXP) 
with a prerequisite of clarity of speech (SP), (c) examples 
and guided practice (EGP), and (d) assessment of student 
learning (ASL). This section presents the evidence for this 
assumption. The inclusion of ORG, EXP, SP, EGP, or ASL in 
parentheses indicates information that supports that assumed 
dimension of teacher clarity. 

Solomon, Bezdek, and Rosenberg (1964) . These 
investigators studied 24 teachers of adult evening classes 
in American government. They observed the teachers, studied 
audio tapes of lessons, and obtained student evaluations of 
the lessons. The learning measures were a factual test on 
the content of the course and a comprehension test not 
related to the course. There was a negligible correlation 
between the second test and teacher clarity. A reasonable 
conclusion is that one should test the course content that 
one teaches (or teach the course content that is to be 
tested). Teacher variables were factor analyzed and one of 
the eight factors was labeled Clarity-Expressiveness vs. 



- 14 - 



- 15 - 

Obscurity- Vagueness. The correlation of this factor with 
achievement gain in the topics taught was .58. The items 
that defined the clarity pole of the factor were as follow: 

1. Understanding of student statements (ASL) ; 

2. Clear and understandable (EXP); 

3 . Coherence ( EXP ) ; and 

4. Well organized (ORG). 

Hiller, Fisher, and Kaess (1969) . These investigators 
made frequency counts of 15-minute content-controlled 
lectures in 12th grade social studies. The five factors 
used were as follow: 

1. Verbal fluency (SP); 

2. Optimal information amount (EXP); 

3. Knowledge structure cues (ORG); 

4. Interest (EXP); and 

5. Vagueness (SP). 

The correlations between vagueness and student learning. were 
r(32) = -.59 and x(23) = -.48. 

Cruickshank, Myers, and Moenlak (1975) (cited in 
Cruickshank & Kennedy, 1986). These researchers set out to 
determine the specific instructional behaviors that students 
use to discriminate between clear and unclear teachers. 
They had 1,009 students in grades 6-9 recall their most 
clear teacher and list the five things that the teacher did. 
This resulted in the following 12 categories: 

1. Providing students with feedback or knowledge of 
how well they are doing (EGP); 



- 16 - 

2. Teaching things in a related step-by-step manner 
(EXP) ; 

3. Orienting and preparing students for what follows 

(ORG) ; 

4. Providing standards and rules for satisfactory 
performance (EGP); 

5. Using a variety of teaching methods (EXP); 

6. Repeating and stressing directions and difficult 
points (EXP); 

7. Demonstrating (EGP); 

8. Providing practice (EGP); 

9. Adjusting teaching to the learner and the topic 
(EXP) ; 

10. Providing illustrations and examples (EGP); 

11. Communicating so that students understand (EXP); 
and 

12. Causing students to organize materials in a 
meaningful way (ORG). 

Bush, Kennedy, and Cruicksh :< ( 1977) . The 110 
low-inference behaviors that were detected by Cruickshank et 
al. (1975) were used to get 1,549 ninth-grade students to 
rate their most clear and unclear teachers. The 10 
behaviors that discriminated best between these two sets of 
teachers were as follow: 

1. Gives the student individual help (EGP); 

2. Gives explanations that students understand (EXP); 

3. Teaches at a pace appropriate to the topic and the 



- 17 - 



students ( EXP ) ; 

4. Takes time when explaining (EXP); 

5. Answers student questions (EGP); 

6. Stresses difficult points (EXP); 

7. Shows students examples of how to do classwork or 
homework (EGP); 

8. Reviews work with students in preparation for a 
test (ORG); 

9. Gives the students enough time to practice (EGP); 
and 

10. Supports the lesson with specific details (EXP). 
Kennedy-/ Cruickshank/ Bush/ and Myers (1978) . The Bush 

et al. (1977) items were used with junior high students in 
Ohio, Tennessee, and Australia. A factor analysis of the 
results produced the following: 

1. Assesses student learning (ASL) ; 

2. Provides student opportunity to practice (EGP); 

3. Uses examples (EGP); and 

4. Reviews and organizes (ORG). 

The 10 most discriminating behavioral statements were as 
follow: 

1. Explains things simply (EXP); 

2. Gives explanations the students understand (EXP); 

3. Teaches at a pace appropriate to the topic and 
students (EXP); 

4. Stays with the topic until the students understand 
(EXP); 



- 18 - 

5. Tries to find out if the students do not understand 
and repeats things (ASL)j 

6. Teaches step-by-step (EXP)j 

7. Describes the work to be done and how to do it 

( EGP ) ; 

8. Asks if the students know what to do and how to do 
it (EGP); 

9. Repeats things when the students do not understand 
(EXP); and 

10. Explains something and then works an example (EGP). 
S. Smith (1978) (cited in Cruickshank & Kennedy, 1986). 

Observers rated videotapes of 99 community college 
instructors. Factor analysis resulted in the following 
factors: 

1. Organization (ORG); 

2. Makes organization of presentation explicit to 
students (ORG); and 

3. Uses guestioning skills, examples (ASL & EGP). 
nines (1981) (cited in Cruickshank & Kennedy, 1986). 

The methods used in the preceding studies were duplicated 
with 573 undergraduate students. The factors produced were 
as follow: 

1. Provides for student understanding and assimilation 
of instructional content (EXP); 

2. Explains/demonstrates how to do the work by the use 
of examples (EGP); and 



- 19 - 

3. Structures instruction and instructional content 
/presents content in a logical sequence (ORG &< EXP). 

Cooper and Foy (1967) . These researchers analyzed the 
responses of university students in England and found that 
the teacher behaviors that the students considered most 
important were as follow: 

1. Presents his material clearly and logically (EXP); 

2. Enables the student to understand the basic 
principles of the subject (EXP); 

3. Can be heard clearly (SP); 

4. Makes his material intelligibly meaningful (EXP); 

5. Adequately covers the ground in the lecture course 

(ORG) ; 

6. Maintains continuity in the course (ORG); 

7. Is constuctive and helpful in his criticism (EGP); 
and 

8. Shows an expert knowledge of his subject (EXP). 
McCaleb and Rosenthal (1983) . These researchers factor 

analyzed both student ratings and observer ratings of the 
instruction of nine college teaching assistants. The three 
main factors produced could be called clarity of assessment 
of student learning (ASL), clarity of examples and guided 
practice (EGP), and clarity of organization (ORG). 

The preceding literature review provides evidence for 
the assumed teacher clarity dimensions: organization, 
explanation, examples and guided practice, and assessment of 
student learning. Clarity of speech is assumed to be a 



- 20 - 

prerequisite of clarity of explanation rather than a 
separate dimension. This will now be reviewed. 
Clarity of Speech 

It is necessary for the teacher to speak loudly enough 
so that the students can hear. This is not often a problem 
but, when it is, it is serious. Another problem can be the 
teacher's accent. This is particularly the case in the 
teaching of science and math at universities where many of 
the professors and graduate teaching assistants are foreign. 
This problem often resolves itself in about two weeks, which 
seems to be the time it takes for students to get used to an 
accent. Teacher behaviors that loaded at .35 or above on 
"verbal fluency" (Bush, Kennedy, & Cruickshank, 1977) were 
as follow: 

1. Speaks grammatically; 

2. Speaks with expression; and 

3. Speaks so that all the students can hear. 

I have not come across any other literature on these two 
problems except for an article on making effective academic 
presentations (Renfrew & Impara, 1989) which stated that 
"verbal behaviors include concerns about pace, pause, pitch, 
vocal variety, and clarity. Make sure the audience can hear 
and understand your words" (p. 21). 

Hiller, Fisher, and Kaess (1969) defined vagueness as 
"a psychological construct which refers to the state of mind 
of the performer who does not sufficiently command the facts 
or the understanding required for maximally effective 



- 21 - 

communication" (p. 670). Kounin (1970) found that 
discontinuities, where the teacher interjects irrelevant 
content or relevant content at inappropriate times, resulted 
in loss in lesson momentum. 

A typical experimental investigation into the effect of 
clarity of speech (called teacher clarity by the 
investigators) is that of Land and L. Smith (1979). They 
used a "2 (teacher vagueness versus no teacher vagueness) x 
2 (teacher mazes versus no teacher mazes) x 2 (additional 
unexplained content versus no additional unexplained 
content) experimental design" (p. 55). The subjects (N = 
150) were education and psychology undergraduates. They 
viewed 20-minute videotaped lessons and then completed a 
17-item criterion- referenced test. The investigators 
reported that the results indicated a significant 
relationship with achievement for vagueness and mazes but 
not for the inclusion of extra content. 

Hiller et al. (1969, quoted in L. Smith & Land, 1981) 

reported the following to be indicators of vagueness: 

Ambiguous designation: Conditions, other, 
somehow, somewhere, someplace, thing. 

Approximation: About, almost approximately, 
fairly, just about, kind of, most, mostly, much, 
nearly, pretty (much), somewhat, sort of. 

"Bluffing" and recovery: Actually, and so forth, 
and so on, anyway, as anyone can see, as you know, 
basically, clearly, frankly, in a nutshell, in essence, 
in fact, in other words, obviously, of course, so to 
speak, to make a long story short, to tell the truth, 
you know, you see. 



- 22 

Error admission: Excuse me, I'm sorry, I guess, 
I 'm not sure. 

Indeterminate quantification: A bunch, a couple, 
a few, a little, a lot, several, some, various. 

Multiplicity: Aspect (s), kind(s) of, sort(s) of, 
type(s) of. 

Negated intensif iers : Not all, not many, not 

very. 

Possibility; Chances are, could be, may, maybe, 
might, perhaps, possibly, seem(s). 

Probability: Frequently, generally, in general, 
normally, often, ordinarily, probably, sometimes, 
usually. (p. 37) 

Chilcoat (1987) included in the vagueness measure the 
use of (a) pronouns when it is not clear to the students to 
whom or what the teacher is referring and (b) "I could tell 
you, but; of course I could; and so on" as part of bluffing. 
The other indicator of lack of verbal fluency — mazes — was 
defined by L. Smith (1977) as false starts or halts in 
speech, redundantly spoken words, and tangles of words. 
Examples are "will enab . . . will get," and "uh. " 

Clarity of speech is assumed to comprise (a) speaking 
in good English loudly enough so that all the students can 
hear, (b) using few vague terms, and (c) having few false 
starts or halts in speech. 
Clarity of Organization 

Brophy (1987) stated that "information presentations 
are often poorly organized, without advance organizers or 
other appropriate structure at the beginning, underscoring 
the main ideas in the middle, or review and summary at the 



- 23 - 

end" (p. 20). The main components of clarity of 
organization are (a) providing explanation of objectives at 
the beginning of the course and lesson, (b) teaching the 
topics that are covered on the posttest (or specified as the 
objectives), and (c) summarizing and reviewing at the end of 
the course or lesson or at the beginning of the following 
lesson (e.g.. Good & Grouws, 1979) Good, Grouws, & Ebmeier/ 
1983) . 

Clarity of Explanation 

Teacher behaviors that loaded at . 35 or above on 

"explaining" factors produced when ninth graders rated both 

clear and unclear teachers (Bush, Kennedy, & Cruickshank, 

1977) and were not the same behavior expressed in different 

words (e.g., "V/orks examples and explains them," and 

"Explains and then works an example") were as follow: 

Gives explanations that the student understands* 
Teaches at a pace appropriate to the topic and the 
students* 

Stresses difficult points* 
Uses common words 
Explains new words 

Writes important things on the board 
Answers student guestions* 
Teaches one thing at a time 
Repeats enough but not too much 

Reviews work with students in preparation for a test* 

Supports the lesson with specific details* 

Explains by telling a story 

Has students make outlines 

Tells humorous stories when explaining 

Shows movies and explains them afterwards. (pp. 55-56) 
Those marked * were reported by Cruickshank and Kennedy 
(1986) as being prime discriminators between clear and 
unclear teachers. 



- 24 - 

The main components of clarity of explanation were 
assumed to be (a) explaining things simply and 
interestingly, (b) stressing difficult points, (c) using 
small steps, and (d) teaching at an appropriate pace. 
Examples and Guided Practice 

Teacher behaviors that loaded at . 35 or above on the 

Explaining by Examples factors produced when ninth graders 

rated both clear and unclear teachers (Bush et al., 1977) 

were as follow; 

Prepares students for what they will be doing next 
Shows students how to do things 
Gives examples and explains them 
Uses common examples 

Shows students examples of how to do classwork or 
homework* 

Reads the directions with the students 

Asks students before they start to work if they know 

what to do and how to do it 

Gives the student enough time to practice* 

Gives the students individual help* 

Explains the answers, to questions 

Shows the student where he/she is wrong 

Works difficult homework problems selected by students 

on the board 

Explains in detail what will be on a test 

Takes time to answer students' questions before a test. 

(pp. 55-56) 

Those marked * were reported by Cruickshank and Kennedy 
(1986) as being prime discriminators between clear and 
unclear teachers. 

The main components of clarity of examples and guided 
practice are (a) showing students examples of how to do 
classwork or homework, (b) answering student questions, (c) 
giving individual help, (d) giving the students enough time 
to practice, and (e) providing the students with feedback on 
how well they are doing. 



- 25 - 

Assessment of Student Learning 

Solomon, Bezdek, and Rosenburg (1964) reported that the 
item with the highest loading on the factor "Obscurity, 
Vagueness versus Clarity, Expressiveness" was "the teacher's 
proficiency at receiving the communications of the students" 
(p. 29). Assessment of student learning is concerned with 
all the ways in which the teacher learns how well the 
students are receiving the message the teacher is 
transmitting; for example, (a) asking questions during the 
presentation, (b) encouraging relevant discussion, and (c) 
checking students' work and tests. 

With the aid of this literature review, the four 
assumed dimensions of teacher clarity (plus the 
prerequisite: clarity of speech) have now been defined in 
terms of observable low-inference teacher behaviors. 

Analysis Literature 
General Problems of Meta-analysis 

Orwin and Cordray (1984) stated that "meta-analysis is 
still a relatively new enterprise, and as such it warrants 
further exploration before conventions regarding proper 
conduct are adopted" (p. 72). Some of the problems that 
have been discussed in the literature are as follow; 

Apples and oranges . "Criticisms of meta-analysis have 
primarily revolved around the the issue of 'combining apples 
and oranges. ' That is, combining the results of different 
studies runs the risk of producing an amalgam that makes no 
conceptual sense" (Slavin, 1984, p. 9). The reply of the 



- 26 - 

meta-analysts (e.g., Glass, McGaw, & Smith, 1981; Hedges & 
Olkin, 1985; Hunter, Schmidt, & Jackson, 1982) is that if 
sets of studies are different in an important way, then the 
sets will produce significantly different effect sizes. If 
interactions between study properties are important, they 
will be detected by regression equations. 

Public availability . An important characteristic of 
the scientific method is public availability of the data and 
of the research process. Bullock and Svyantek (1985) 
suggested that (a) the list of studies used in the analysis, 
(b) the coding rules used to convert effect-size 
characteristics into measured variables, and (c) possibly 
copies of the analyses performed should be publicly 
available. 

Publication bias . Studies published in journals and 
books tend to be biased toward positive findings (M. Smith, 
1980). One must search for other studies in ERIC (Education 
Resources Information Center) and similar indexes and in 
Dissertation Abstracts International (Kraemer & Andrews, 
1982). 

Selection of studies . Care should be taken that 
selection of studies is not biased by the reviewer's 
preferences as to what constitutes good methodology. All 
studies that meet the criteria for inclusion (which must be 
given) must be included. Studies which fail to meet the 
criteria should be cited and the reason for exclusion stated 
(Kraemer & Andrews, 1982). 



- 27 - 

Treating effect sizes from the same study as 
independent . M. Smith and Glass (1977) claimed that 
treating nonindependent data as if they were independent has 
no consistent impact on the mean effect size. This claim is 
supported by the reanalysis performed by Landman and Dawes 
( 1982) . 

Differential attrition . "The important question is 
whether there is differential attrition in the . . . groups" 
(Landman & Dawes, 1984, p. 72). This problem is 
particularly important in college classes: If a larger 
number of poor students drop one class than drop another, 
the difference between class mean achievement gains is not a 
fair comparison of the mean student learning in the classes. 
A reasonable solution would be to assign, to those students 
who drop, an achievement gain two class standard deviations 
below the class mean. 

Coding . Detailed decision rules are required for 
nominal values, for combinations, and for missing data. If 
estimates are made from published data, the methods of 
estimation should be explicit. Percentage agreement between 
independent coders should be given (Bullock & Svyantek, 
1985). 

Domain of generalization . It is the meta-analyst ' s 
right to define the domain of generalization, but it is 
important that conclusions be limited to that domain 
(Bullock & Svyantek, 1985). 



- 28 

Criteria for Evaluating Meta-analyses 

Bullock and Svyantek (1985) suggested the following 
criteria for evaluating meta-analyses: 

1. Uses a theoretical model as the basis of the 
meta-analysis ; 

2. Identifies precisely the domain to be tested; 

3. Includes all publicly available studies in the 
defined content domain; 

4. Avoids selecting studies based on criteria of 
methodological rigor, age of study, or publication 
status; 

5. Publishes or makes available the final list of 
studies used; 

6. Selects and codes variables on theoretical grounds 

7. Provides detailed documentation of the coding 
scheme including estimation procedures used for 
missing data; 

8. Uses multiple raters to apply the coding scheme 
and provides a rigorous assessment of interrater 
reliability; 

9. Reports all variables analyzed; 

10. Publishes or makes available the data set used in 
analysis ; 

11. Considers alternative explanations for the 
findings obtained; 

12. Limits generalization of results to the domain 
specified by the research; 

13. Reports study characteristics in order to 



- 29 - 

understand the nature and limits of the domain 
actually analyzed; and 

14. Reports the entire study in sufficient detail to 
allow for direct replication. 
Criticisms of Meta-analyses in Education 

Slavin (1984) criticized not the concept of 
meta-analysis but how it had been used in practice in 
education. His criticisms included the following: 

Carlberg and Kavale (1980) . These researchers 
investigated the achievement and social outcomes of 
placement of exceptional children in special classes rather 
than in mainstream classes. In most studies the children 
who were compared were matched only on IQ. Slavin pointed 
out that there were probably other reasons (such as 
behavioral difficulties) tor placing one student in the 
special class rather than in the mainstream so that the 
comparison would inevitably lead to an apparent advantage 
for mainstreaming. 

Kulik and Kulik (1982) . These researchers investigated 
effects of the tracking (class ability grouping) of students 
by IQ and prior achievement. Slavin stated that (a) in one 
study no account was taken of a difference of eight IQ 
points in the groups of students being compared and (b) that 
in comparing high achievers in high-track classes with high 
achievers in low-track classes no account was taken of why 
these high achievers were on different tracks. The mean 
effect in studies using random assignment of students was 
zero; thus Kulik and Kulik 's claim for the superiority of 



- 30 - 

tracking on achievement rests solely on studies that are 
most likely to suffer from selection bias. 

Johnson/ Johnson, and Maruyama (1983) . This analysis 
investigated the effects of cooperation on relationships 
between students. Slavin's main complaint was that in the 
cooperation groups the students were instructed to work 
together and in the competition groups the students were to 
work alone. The conclusions of the analysis were (a) that 
there was more cooperation in the cooperation groups and (b) 
that groups of students solved problems qioicker than did 
individual students. These conclusions are trivial. 

Glass, Cahen, Smith, and Filby (1982) . This study 
purported to study the effects of class size on achievement. 
In classes of normal size (20-40) there was practically no 
effect, but when normal-size classes were compared to 
tutoring (1-3 students) there was a considerable effect. 
Glass et al. concluded that class size did have a meaningful 
effect on achievement when this was patently not true in the 
normal range of classes. 

These early meta-analyses show that it is just as 
important in meta-analysis as it is in other analyses to be 
certain that the data are really addressing the problem that 
one should be investigating. 



CHAPTER III 
REVIEV'f OF METHODOLOGY 



^ Ob jective 

The objective of a meta-analysis of correlational 
studies is to determine the best estimate of the confidence 
interval surrounding the correlation between one variable 
and another from all available information in the 
literature. The confidence interval is the value of R +/- 
d£, where R is the point estimate of the population 
correlation, dR is the uncertainty in that estimate, and +/- 
means plus or minus. 

The result of the meta-analysis might be just one 
confidence interval that covers all the circumstances 
covered in the studies or, more often, a number of 
confidence intervals that are valid in specific 
circumstances. For example, the result of this study might 
have been that the correlation between teacher clarity and 
student achievement gain is .35 +/- .05, whatever the 
definition of teacher clarity and whatever achievement is 
being measured. More likely, a different confidence 
interval is obtained when teacher clarity is defined as 
clarity of explanation rather than clarity of organization, 
or when achievement is based on student verbal ability 
rather than on student numerical ability. 



- 31 - 



- 32 - 

The purpose of this chapter is to discuss the methods 
that have been proposed for determining the best values of R 
(point estimate of correlation) and dR (the uncertainty) and 
for determining whether one confidence interval (distibution 
of R) is significantly different from another. 

Uncertainty and Variance 
The confidence interval is the range of values that we 
can be 95% confident includes the true value of the 
population correlation, R.. If the confidence interval does 
not include .00, the value of R is significant at the .05 
level. The uncertainty in a mean or correlation is given by 
twice the standard deviation in the mean (standard error) or 
correlation provided the value of N (the number of whatever 
is the unit of analysis) is at least about 20. The variance 
is the square of the standard deviation. Thus, discussing 
the reduction in the variance is the same as discussing 
reduction of the uncertainty or narrowing of the confidence 
interval. 

Summary of Procedure 
The meta-analytic procedure consists of (a) defining 
the problem; (b) finding the studies; (c) describing, 
classifying, and coding research studies; and (d) analyzing 
the data. There is no disagreement on how to carry out the 
first three of these but much disagreement about the 
techniques of analysis. I will (a) define the problem and 
describe the methods used for finding and describing the 
studies and (b) detail the techniques of analysis 



- 33 - 

recommended by Glass, McGaw, and Smith (1981); Hunter, 
Schmidt, and Jackson (1982)? Hedges and Olkin (1985); and 
Hedges (1988). The teacher-clarity data have been 
analyzed by the different techniques in order to determine 
whether (in this case) they lead to substantively 
different conclusions. 

Defining the Problem 
In this dissertation, the problem was to determine 
the correlation between teacher clarity and the mean class 
student learning (achievement gain) in normal 
public-education classes in English-speaking, 
industrialized countries (e.g., USA, UK, and Australia). 
The grade range was assvuned to be from Grade 1 through 
undergraduate study (called Grade 13). A normal class was 
defined as one in which the students are not special in 
any way and the class runs for a minimum of 6 weeks. 
Class achievement gain was defined as the mean posttest 
score minus the mean pretest score on a valid (relevant), 
reliable test of the subject matter that was taught on the 
course. Teacher clarity was defined in the ways given in 
Table 3-1. 

The criteria for the inclusion of an effect size 
(correlation) from a study were (a) the unit of analysis was 
the class rather than the individual student, (b) a common 
achievement measure (that covered the content taught) was 
used across all classes, and (c) data were available to 
calculate the correlation between the rating of the teacher 



- 34 - 



Table 3-1. Teacher Behaviors Defining the Dimensions 

of Teacher Clarity - 



Clarity of Speech (SP) 

(Focus is on absence of inhibitors of communication) 

1 . Speaks so that all the students can hear 

2. Speaks good English without a marked accent 

3. Uses few vague terms 

4. Speaks with few mazes (false starts or halts in speech, 
e.g., "uh") 



Clarity of Organization (ORG) 

(Focus is on objectives, content coverage, and review) 

1. States objectives of the course and lesson 

2. Covers all the topics on the posttest: Teaches the topics 
that are specified as the objectives of the course 

3. Reviews the lesson at the end of the lesson or at the 
beginning of the following lesson. 

4. Reviews work with students in preparation for a test 



Clarity of Explanation (EXP) 

(Focus is on the teacher's presentation) 

1 . Explains things simply and makes them interesting 

2. Repeats and stresses directions and difficult points 

3. New content is introduced in small steps and is related 
through ideas held in common by contiguous discourse units 
(kinetic structure) 

4. Teaches at a pace appropriate to the topic and the 
students 

Examples and Guided Practice (EGP) 

(Focus is on the teacher's efforts to help the student) 

1. Shows students examples of how to do classwork or homework 

2. Answers student questions 

3. Gives the students individual help 

4. Gives the students enough time to practice 

5. Explains points that have not been answered well on 
classwork, homework, and tests 

6. Provides standards and rules for satisfactory performance 

7. Provides students with feedback or knowledge of how well 
they are doing 



Assessment of Student Learning (ASL) 

(Focus is on communication from student to teacher) 

1 . Asks the students questions during the presentation 

2,. Encourages relevant discussion 

3. Checks students' classwork, homework, and tests 



- 35 - 

on at least one dimension of teacher clarity and the class 
mean achievement gain. Effect sizes based on posttest only 
were included, but it was recorded whether or not there was 
evidence of the random assignment of students to classes. 

The problem was extended to determine whether the 
correlation between teacher clarity and class learning 
depends on such variables as (a) the definition of teacher 
clarity, (b) the student ability (verbal or quantitative) on 
which achievement in the subject depends, (c) teacher 
experience (teacher/professor versus student 

teacher/teaching assistant), (d) the grade level, (e) normal 
or experimental class, and/or (f) the characteristics (e.g., 
quality and year) of the study. 

Finding the Studies 
The methods used to find the studies for this analysis 
were (a) tracing back from the references in the studies 
already located, especially review studies; (b) conducting 
computer searches of indexes such as ERIC (Education 
Resources Information Center), Dissertation Abstracts , 
Psychological Abstracts , and NTIS (National Technical 
Information Service); (c) supplying a bibliography to 
researchers in the field and asking them to let me know of 
any studies I had missed; and (d) manually searching recent 
editions of likely journals that have not yet been added to 
the indexes. 



- 36 - 

Describing, Classifying, and Coding Research Studies 
The Dimensions of Teacher Clarity 

The concept of teacher clarity of communication in this 
dissertation is based on communication (information) theory 
(see chapter I). The basic idea is that the teacher makes a 
clear, well-organized presentation of the material. The 
teacher then gets the students to practice answering 
guestions of the type that are on the posttest and provides 
guidance to the students while they are practicing. Any 
efficient system must have a feedback mechanism. In this 
communication system the feedback during the presentation is 
obtained by the teacher asking the students questions. 
During practice sessions feedback is obtained by the teacher 
encouraging student discussion and by the teacher checking 
the students' classwork, homework, and tests. The teacher 
then reviews with the students topics and methods, that 
require extra work. 

Communication between teacher and student cannot be 
clear if the student cannot hear or understand the teacher. 
Thus, lack of clarity of speech is regarded as an inhibitor 
of teacher clarity. If clarity of speech is a problem, 
other dimensions of teacher clarity do not get the chance to 
operate efficiently. 

On the basis of this theory and on the basis of the 
literature reviewed in chapter II, teacher clarity is 
assumed to have the dimensions given in Table 3-1. Clarity 
of speech has already been dealt with. Clarity of 



- 37 - 

organization focuses on statement of objectives, the content 
coverage (in terms of the topics on the posttest) of the 
lesson and course, and on review. Clarity of explanation 
focuses on the teacher's presentation (keep it simple, 
stress difficult points, use small steps connected to each 
other, and go at the correct pace). Clarity of examples and 
guided practice focuses on the teacher's efforts to help the 
students answer test-like questions by giving them 
opportunities to practice, showing examples on the board, 
and by assisting individuals or groups. Assessment of 
student learning is the feedback from student to teacher and 
comprises asking the students questions, encouraging 
relevant discussion, and checking classwork, homework, and 
tests. 

Some teacher- behavior factors that were used in the 
studies are a mixture of the above dimensions. In this case 
one must decide whether the factor is predominantly one of 
the above dimensions or whether it is such a mixture that it 
is better to categorize it as such and call it teacher skill 
(SKI) . 

The following decision-making procedure ^^ras found 
useful in categorizing the teacher behaviors reported in the 
studies: 



- 38 - 



Was the teacher behavior concerned with whether 

1 . the students hear and understand the 
teacher's speech? (loud enough, accent not too 
foreign, few vague terms, few false starts, few 

. 1 

2. the teacher was asking a question, 
listening to answers or discussion, or checking 
student work? 



"uh"s) 

<= N0< 1 >Yes => SP 



<= N0<- 



_>Yes => ASL 



3. the teacher was stating objectives or 
conducting review? (including relevance of 
objectives to posttest and relevance of teaching to 



objectives ) 

I 



i 



<= NO 4 1 ^Yes => ORG 



4.. the teacher's presentation of the topic 
was simple, interesting, used small steps that were 
related to each other, was conducted at the 
appropriate pace, and emphasized the important 
points? (the teacher has mastered both the topic 
and teaching the topic) 



i 



^<= N0< 1 ^Yes => EXP 

5. the teacher was helping the student to 
perform at the level required by the posttest? 
(showing by example, answering questions, providing 
practice time, setting standards, informing 
students of their level of performance) 

|<= NO 4 _1 >Yes => EGP 

6. the teacher was acting in ways covered by 
more than one of the above dimensions and one of 
the dimensions did not predominate over the others? 

NO4 . 1 ^Yes => SKI 

Try again. 

The teacher behaviors in the studies were assigned 
dimensions independently by four coders. The reliability of 
classification was found to be .76. 



- 39 - 

Description and Classification 

The results of describing and classifying studies are 
presented in Appendix A. In Table A-1, the information 
given is an identification number (ID), the name(s) of the 
author (s) and the year of publication, the teacher-clarity 
behaviors given in the study, and the assumed dimensions of 
the behaviors. In Table A-2, after the ID number, the 
information given is as follows: 

1. VER refers to whether the subject taught is 
classified as achievement being mainly dependent on the 
student's verbal ability (1) or numerical ability (-1). 

2. PUB refers to whether the study was published in a 
journal or book (1), or is a dissertation or has not been 
published (-1) (e.g., ERIC microfiche of an address at an 
annual meeting). 

3. STU refers to whether the rating of teacher clarity 
was made by students (1), by observers (-1), or by both 
students and observers (0). 

4. ACH is an estimate of the validity of the 
comparison of the achievemnent gain of one class with 
another. In order to validly compare the achievement gain 
of one class with that of another, one should ideally have 
the same students in each class or at least have random 
assignment of students to classes. This is often not 
possible, so an effort .is made to compensate for initial 
differences in students by measuring the difference between 
the actual achievement of the students and the achievement 



- 40 - 

that could be predicted from their initial characteristics, 
that is, a residual gain score. If no account is taken of 
initial differences, a simple gain score (difference between 
posttest and pretest) is used. If only a posttest is used 
(and there is no evidence of the random assignment of 
students), the validity of comparison of the achievement 
gain is likely to be low. This is especially so if the 
achievement measure is of the essay type rather than of the 
objective type. The worst situation is when the achievement 
measure is of the essay type and is graded by the class 
teacher. With this in mind, the following codes are used: 
Essay test rated by the class teacher (0), posttest only 
with no random assignment of students (1), simple gain score 
(2), residual gain score (3), random assignment of students 
or the same students rate all the teachers (4). 

5. REL is the reliability of the teacher-clarity 
measure. 

6. TEX refers to whether the teachers were experienced 
(1), were student teachers or teaching assistants (-1), or 
were both ( 0) . 

7. WKS is an estimate of the number of weeks between 
the start of the course and the posttest. 

8. GRA is the grade (college =13, 8.5 = grades 8 and 

9). 

9. NS is the average number of students in a class. 

10. NC is the number of classes in the study. 



- 41 - 

11. DIM is the assumed dimension of teacher clarity: 
ORG indicates clarity of organization, EXP indicates clarity 
of explanation, EGP indicates examples and guided practice, 
ASL indicates assessment of student learning, SP indicates 
clarity of speech, and SKI indicates a factor score 
comprising more than one dimension and no one dimension is 
dominating the factor. 

12. R is the correlation between the dimension of 
teacher clarity and the achievement gain of the class (the 
average value of all the rs reported in the study for the 
dimension, the grade, and the subject area). 

13. SMR is the study mean value of R obtained by 
averaging over all the Rs for the study. 

14. SMZ is the Fisher z equivalent of SMR.' 
Coding Nominal Values 

Nominal values have to be coded to use them in 
regression equations. Is it better to compare the 
correlation (effect size — ^r^) in one category with the 
correlation in another (dummy coding) or with the mean 
correlation over all the studies (effect coding)? I decided 
to use effect coding. For example, in coding the dimensions 
of teacher clarity, the coding shown in Table 3-3 was used. 
If all the variables are coded -1, the dimension is SKI. If 
X is coded 1 and all the other variables are coded 0, the 
dimension is EXP, and so on. 



- 42 - 



Table 3-2. Coding the Dimensions of Teacher Clarity 



Code 



Dimension 


X 


0 


G 


A 


s 


SKI 


-1 


-1 


-1 


-1 


-1 


EXP 


1 


0 


0 


0 


0 


ORG 


0 


1 


0 


0 


0 


EGP 


0 


0 


1 


0 


0 


ASL 


0 


0 


0 


1 


0 


SP 


0 


0 


0 


0 


1 



Techniques of Analysis 

Looking at the Data 

An indispensable approach to understanding one's 
results is to study plots of the data and of residuals 
(Pedhazur, 1982). In this case the frequency distribution 
of the effect sizes (correlations) was plotted and the 
effect sizes were also plotted as a function of each of the 
study variables. The correlations between the variables 
were also obtained. The plots and correlations were studied 
to see v/hat relationships were suggested. 

The effect sizes were also regressed on all of the 
study variables. The residuals (difference between 
predicted and actual value in standard deviation units) were 
plotted as a function of the predicted value. In 
approximately normal data, the residuals are randomly • 
scattered about the zero line, and about 95% of them are 
less than two standard deviations from the line. 



- 43 

The outliers (those data more than two standard 
deviations from the line) were checked to see if any 
mistakes could be detected in analyzing those studies. No 
mistakes were detected, so the data were removed from the 
data set and the properties of the studies were reported 
that might explain the difference between the effect size(s 
produced by a study (or group of studies) and the majority 
of the studies. The new data set was regressed on the 
variables as before, and the residuals were studied for 
curvature or heteroscedasticity to see if any function of 
any of the independent variables (e.g., log X, 1/X, or ) 
was suggested as a good predictor of the effect size. 
Problems in Accumulating the Effect Sizes from Each Study 

For fully independent effect sizes each study would 
produce just one effect size, and that effect size would 
refer to just one dimension of teacher clarity, one grade 
level, one subject area and so on. In practice the average 
of all the effect sizes in the study is used or each effect 
size is treated as independent of the study from which it 
came. The way in which these problems are treated by Glass 
McGaw, and Smith (1981); by Hunter, Schmidt, and Jackson 
(1982); and by Hedges and Olkin (1985) and Hedges (1988) 
will now be explained. 
Glass, McGaw, and Smith (1981) 

Glass et al. (1981) stated that it makes no practical 
difference whether one accumulates r,s, r s or ^s so the 
analyst should do whichever she or he prefers. On the 



- 44 - 



problem of the nonindependence of the effect sizes they 
stated 

"Studies" cannot be considered the unit of data 
analysis without aggregating findings above the levels 
at which many interesting relationships can be studied. 
. . . There is no simple answer to the guestion of how 
many independent units of information exist in the 
larger data set. . . . Two resolutions of the problem 
can be envisioned: one risky, the other complex. 

The simple (but risky) solution is to regard each 
finding as independent of the others. The assumption 
is untrue, but practical. (p. 200) 

One complex method suggested by Glass was Tukey's 
jackknife method. In this method (a) the mean correlation 
is determined using all the studies and multiplied by the 
number of studies (K), (b) the means with the effect sizes 
from each study deleted in turn are multiplied by K-1, and 
(c) the values in (b) are subtracted from the value in (a) 
to give K pseudo study correlations from which the 
uncertainty can be determined. 
Hunter, Schmidt, and 'Jackson (1982 ) 

Hunter et al. considered that it is correct to cumulate 
rs rather than z.s. They state that z^s give larger weights 
to large correlations than to small ones resulting in a 
positive bias of up to .07 in the mean correlation. 

Concerning the problem of using individual correlations 

or the study average correlation, they stated 

If a set of indicator variables is statistically as 
well as psychologically equivalent . . . , then the 
ideal cumulation within a study is confirmatory factor 
analysis with communalities . . . . The average 
correlation is usually noticeably poorer. If the set 
of indicators deviates considerably from the unifactor 
model, then the set of individual correlations should 
be contributed to the larger cumulation. . . . The 
unifactor hypothesis cannot even be tested for many 



- 45 - 

studies. For such studies the choice defaults to the 
use of individual correlations versus the use of the 
average correlation. (pp. 122-123) 

I chose to average correlations within studies/ within 

teacher-clarity dimensions, and within grades and subject 

areas. For the overall confidence interval these values 

were averaged to give a study mean which was used in the 

cumulation procedure. 

The Hunter cumulation procedure is 

1. The frequency-weighted mean (frequency is number of 
classes) and variance of the effect sizes are corrected for 
(a) sampling error, (b) error of measurement, and (c) range 
variation. 

2. If considerable variance remains after the 
adjustment for statistical artifacts, the correlations are 
examined for evidence of moderator variables or are analyzed 
by subsets. Selected properties that vary across studies 
are coded and correlated with study rs. One relies upon 
theoretical, logical, statistical, and psychometric 
considerations when possible in deciding what study 
characteristics to code and how to code them. 

3. Correlations among characteristics and the 
regression of jc on study characteristics are computed. The 
resulting beta weights are interpreted as indicating 
potential causal effects of true study characteristics on 
true study Rs. 

Rationale of the Hunter and Schmidt method . The 
correlation between teacher clarity and achievement gain 



- 46 - 

varies with the circumstances, such as numerical or verbal 
subject, grade level, and teacher experience. Thus, there 
is a population distribution of correlations with a mean of 
R_ and a standard deviation of S.. Field-study attempts to 
determine this distribution introduce sampling error, error 
of measurement, and (possibly) range restriction that 
increase the standard deviation (or variance = standard 
deviation squared) and (in the case of measurement error and 
range restriction) shift the value of the mean. This 
produces a sample distribution with a mean of _r and a 
standard deviation of s_. 

The goal is to estimate the variance introduced by 
sampling error and subtract it from the observed variance. 
The mean and variance are also adjusted for the effects of 
measurement error and (when necessary) range restriction. 
The result is an estimate of the population distribution. 
This population can then be tested for homogeneity and, if 
necessary, split into smaller data sets. 

Correction for sampling error . The formula for the 
expected sampling error variance is ( 1 - _R^)^ x K/N, where R_ 
is the mean correlation, K is the number of effect sizes 
that have been entered into the cumulation, and N is the 
total number of classes. 

Correction of the confidence interval for error of 
measurement . The mean can be corrected for error of 
measurement by dividing the obtained mean value of x by the 
square roots of the mean reliabilities of the two measures. 



- 47 - 

In this case I am interested in the correlation between true 
teacher clarity (see later) and measured achievement gain, 
that is, the achievement gain as it is usually measured in 
practice rather than the achievement gain that would be 
obtained if it were measured without error. I have, 
therefore, corrected for error of measurement in teacher 
clarity only. (True teacher clarity is defined as the mean 
rating the teacher would receive from an infinite number of 
students being taught under the same circumstances as those 
actually being taught.) 

Correction for restriction in range . When the range of 
values of the independent variable (teacher clarity) is not 
the same as the range for which one wishes to estimate a 
correlation, a correction can be made using a function of 
the ratio of the standard deviation of the observed values 
to the standard deviation of the desired range. In this 
analysis the observed range of teacher clarity is assumed to 
be the range that occurs in the population of teachers so 
that there is no need to make this correction. 

Test of homogeneity of Rs . The test statistic is N 
(number of classes) times the observed variance divided by 
(1 - ^ )^ . The test statistic is compared to the 5% value 
of Chi sguared with (K - 1) degrees of freedom where K is 
the number of effect sizes. If the test statistic is ■ 
smaller than the value of chi squared, the effect sizes are 
not diverse. Hunter et al. stated that this is strong 
evidence that there is no true variation across studies 



- 48 - 

(Hedges and Olkin, 1985 take a different view — see later). 

Analysis by subsets . If the effect sizes are found to 
be diverse, it is necessary to split the data set into sets 
divided by such variables as (a) verbal or numerical 
subject, (b) dimension of teacher clarity, or (c) teacher 
experience, and to repeat the above procedure on these 
reduced data sets. 

Correlations among characteristics and the regression 
of the values of r on study characteristics . The 
correlations among characteristics and the regression of r; 
on the characteristics tell us more about the dependence of 
jr on these variables. 
Hedges and Olkin (1985 ) 

Hedges and Olkin showed that the Pearson r; is a biased 
estimate of the true correlation with the bias estimated as 
-r(l - r2)/2N. The magnitude of this bias is less than .01 
when the value of _r is about .3 and the value of N is 
greater than 18. I, therefore, do not need to be concerned 
with this bias in this analysis (average number of classes 
per study is 41 ) . 

The sampling variance of _r is estimated by ( 1 - r^)2/N. 
In order to make the variance independent of the value of r, 
Hedges and Olkin stated that the r,s should be converted to 
Fisher zs before cumulation where 

z = .51og((l + r)/(l - r)) 
and the number of degrees of freedom is (N - 3). The lower 
value of the 95% confidence interval of Z_ (the mean of z) is 



- 49 - 

given byZ^ - 2/(N - 3)^/2 a^d the upper value by 
1/2 

Z. + 2/(N - 3) . This confidence interval is then 
converted to a confidence interval in R_ (mean of r) using a 
table of conversions. 

Cumulation procedure . The rs are converted to _zs and 
the weighted sample mean Z_ found using weights (NC - 3) 
where NC is the number of classes in the study. This sample 
mean is used as the estimate of the population mean. The 
population variance in this mean is given by 1/(N - 3K) 
where N is the total number of classes and K is the number 
of effect sizes used in the cumulation. Note that this 
method ignores the observed variance in the _zs in favor of a 
formula. This is not very good science: If theory 
(formula) provides one value and experiment (observation) 
another, the experimental value is valid until it can be 
shown that there is something wrong with the experiment. 

Measurement error . The value of the new mean is found 

by dividing by the square root of the reliability of the 

measurement of teacher clarity. 

Test of homogeneity of zs . The test statistic is the 
2 

sum of (NC - 3)d where d is (^ - z) • The test statistic is 
compared to the 5% value of chi squared with (K - 1) degrees 
of freedom where K is the number of effect sizes. Hedges et 
al. warned the reader not to take this test too seriously: 
If the number of effect sizes is large, even small variation 
can produce a significant value of chi- squared. (Hunter 
takes a different view — see earlier.) 



- 50 - 

Fitting general linear models to the zs . A weighted 
least sguares procedure is used with the weight specified as 
(NC - 3). The number of predictors (p) must be less than 
the number of effect sizes used (K). The chi-squared 
statistic for testing the model specification is the "error 
sum of squares." If this value is less than the 5% value of 
chi squared with (K - p) degress of freedom, the model is a 
good fit. 
Hedges (1988) 

The population variance in z is given by the observed 
variance minus the expected variance. The expected variance 
is given by K/(N - 3K) where N is the total number of 
classes and K is the number of studies. To correct for the 
unreliability in the measurement of teacher clarity, the 
variance is divided by the reliability of teacher clarity, 
or the point estimates of the limits of the 95% confidence 
interval are divided by the square root of the reliability. 
Summary of Analyses Used in This Study 

Data inspection . The freguency distribution of the 
effect sizes and the frequencies of categories were 
determined. Effect sizes (rs) were plotted and regressed on 
study characteristics. Trends and residuals were inspected, 
and outliers were removed from the data set. 

Glass et al. (1981) . The methods are as follow: 

1. Acctimulate r_s. 

2. Find unweighted mean and variance treating each 
effect size as independent. Assume that this mean and 



- 51 - 

variance are estimates of the population mean and variance. 
(Correct for measurement error in teacher clarity.) 

3. Find unweighted mean and variance using Tukey's 
jackknife method. (Correct for measurement error in teacher 
clarity. ) 

4. Determine variation of confidence interval with 
effect-size characteristics by analyzing in subsets and 
using unweighted regression eguations. 

Hunter et al. (1982) . The methods are as follow: 

1. Accumulate r^s. 

2. Find weighted mean (R) and variance using the 
average value from each study and weighting by the number of 
classes — NC. 

3. Estimate the population variance by subtracting the 
estimated sampling error variance — (1 - R,*')"' x K/N, where K 
is the number of effect-sizes (studies in this case) and N 
the total number of classes. 

4. Correct the mean and population standard deviation 
for measurement error in teacher clarity by dividing by the 
square root of the mean reliability reported in the studies. 

5. Conduct a test of homogeneity: The test statistic 
is N (number of classes) times the observed variance divided 
by (1 - ^ )^ . The test statistic is compared to the 5% 
value of chi squared with (K - 1) degrees of freedom where K 
is the number of effect sizes. 

6. Determine variation of confidence interval with 
effect-size characteristics by analyzing in subsets and 
using weighted — NC regression equations. 



- 52 - 

Hedges et al» (1985) . The methods are as follow: 

1. Accumulate zs [z = .51og((l + r)/{l - £) ) ] . 

2. Find weighted (NC - 3) mean using the average z 
from each study. Ignore the observed variance and assume 
the variance in the mean is given by 1/(N - 3K), where N is 
the total number of classes and K is the number of studies. 
Assume that these are the population values of mean and 
variance . 

3. Correct the mean and standard deviation for 
measurement error in teacher clarity. 

4. Conduct a test of homogeneity: The test statistic 
is the sum of (NC - 3)d^ where d is (Z - z) and _Z is the 
mean uncorrected for measuring error. The test statistic is 
compared to the 5% value of chi-squared with (K - 1) degrees 
of freedom where K is the number of effect sizes. 

5. Determine variation of confidence interval with 
effect-size characteristics by analyzing in subsets and 
using weighted (NC - 3) regression equations. The number of 
predictors (p) must be less than the number of effect sizes 
used (K). 

Hedges (1988) . Method: Same as in Hedges et al. 
(1985) except that K/(N - 3K) is used as the estimated 
sampling variance and is subtracted from the observed 
variance to estimate the population variance. 



CHAPTER IV 
RESULTS AND ANALYSES 



Results 

The results for the studies that met the criteria for 
inclusion in the meta-analysis (see chapter 3) are shown in 
Table A-1 of Appendix A. There are 47 studies, of which 8 
report only a reliability of the measure of teacher clarity. 
There are 39 studies reporting 110 values of the mean 
correlation between one dimension teacher clarity and class 
achievement gain. Where a study reported the results 
separately for verbal subjects (VER) and numerical subjects 
(NUM), the effect sizes for each are given separately. This 
is also the case when a study reported separate results for 
different grades. Within each of these categories the mean 
of all the correlations in a particular teacher-clarity 
dimension is the effect size for that dimension. The study 
mean over all the effect sizes is also reported for each 
study. 

Studies that were judged to have failed to meet at 
least one of the criteria for inclusion are given in Table 
B-1 of Appendix B. This table gives the reason for 
rejecting the study. 



- 53 - 



- 54 - 

Frequency Distribution 

The frequency distribution of 109 effect sizes (one 
value of -.73 not included) is shown in Figure 4-1. The 
fact that the distribution is bell-shaped (rather than 
looking as though the left side has been cut off) indicates 
that it is not likely that the publicly available studies 
tend to be only those vith significant positive 
correlations . 
Removal of Outliers 

The values of r. were regressed on all the study 
characteristics using the SAS (Statistical Analysis System) 
GLM (General Linear Models) program (see later) and the 
residuals plotted against the ID number of the study. A 
residual is the difference between the actual value of and 
its value predicted by the regression model. Ten values of 
£, were found to have residuals greater than two standard 
deviations of the residuals and were removed from the data 
set. 

Low outliers . The characteristics of the studies that 
produced low outliers are given in Table 4-1. There does 
not seem to be anything in common with these seven studies : 

In four the subject is numerical, two verbal, and one 

both. 

Three different dimensions of teacher clarity — EXP, 
ASL, and EGP — are represented. 

Two were published in a journal or book, four were in 
ERIC, and one was a dissertation. 



- 55 - 



N 



Total N 



109 



-.3 -.2 -.1 .0 .1 .2 .3 .4 .5 .6 .7 .8 

Figure 4-1 . Frequency Distribution of the Effect Sizes 



- 56 - 



Table 4-1. Characteristics of Studies Producing Lov O^^l^^ff 



TD 


VKR 


PUB 


STU ACH 


REL 


TEX 


WKS 


GRA 


NS 


NC 


DIM 


R 


SMR 




8 


-1 


1 


-1 2 


• 


1 


30 


5 


30 


36 


EXP 


-.18 


. 1 Z 


. 1 ^ 


22 


1 


1 


-1 3 


• 


1 


2 


4 


25 


16 


ASL 


-.19 


.10 


1 n 
. iU 


23 


1 


-1 


1 0 


• 


-1 


10 


13 


17 


31 


EXP 


-.19 


-.04 


- . 04 


30 


-1 


-1 


-1 3 


.75 


1 


oU 


Z . D 








-.14 


.12 


.12 


34 


0 


-1 


-1 3 


.45 


1 


20 


5.5 


30 


26 


ASL 


-.73 


-.19 


-.19 


35 


-1 


-1 


-1 2.5 


.45 


1 


30 


5 


20 


41 


EGP 


-.2 


.18 


.18 


42 


-1 


-1 


1 3 


.89 


-1 


10 


13 


30 


36 


EXP 


-.25 


-.25 


.26 



Note. ID: identification number of study; VER: 1 = subject 
b^d on students' verbal ability, -1 = subject based on 
students' numerical ability, 0 = both subject areas; PUB: 
1 = study published in a journal or book, -1 = dissertation or 
ERIC; STU: 1 = student rating of teacher clarity, 
-1 = observer rating of teacher clarity, 0 = rating by both 
students and observers; ACH: 0 = essay test rated by the class 
teacher, 1 = posttest only with no random assignment of 
students, 2 = simple gain score, 3 = residual gain score, 
4 = random assignment of students or the same students rate 
all the teachers; REL = reliability of the teacher-clarity 
measure; TEX: 1 = experienced teachers, -1 = student teachers 
or teaching assistants; WKS = weeks between the start of the 
course and the posttest; GRA = grade (college - 13, 
8.5 = grades 8 and 9); NS = average number of students in a 
class; NC = number of classes in the study; DIM: ORG = clarity 
of organization, EXP = clarity of explanation, EGP = examples 
and guided practice, ASL = assessment of student learning, 
SP = clarity of speech, SKI = a factor score comprising more 
than one dimension and no one dimension is dominating the 
factor; R. = the correlation between the dimension of teacher 
clarity and the achievement gain of the class (the average 
value of all the rs reported in the study for the dimension, 
the grade, and the subject area); SMR = the study mean value 
of R. averaged over all the R.s for the study; SMZ = the Fisher 
z eguivalent of SMR. 



- 57 - 

Four used residual gain measures of achievement, one 
simple gain, one both residual and simple gain, and one 
posttest only. 

In five the teachers were experienced and in the other 

two were learners. 

Five of the courses were normal (6 weeks or more) and 
one experimental. 

Grade level varied from 4 through 13 (college). 

Both the number of students (NS) and the number of 
classes (NC) were quite high (greater than 16) in all the 
studies. 

The nearest suggestion to something in common is that 
teacher clarity was rated by observers rather than by 
students in five of the studies, and there is something not 
quite satisfactory in the two studies that were rated by 
students: 

In Benton (1975) the achievement measure was an essay 
test that was graded by the class teacher. This achievement 
score is likely to be less valid than most of the estimates 
of class achievement gain because of the lower reliability 
of essays compared to objective measures of achievement. 

In Hazelton (1980) 19 factors were produced from the 
answers to 108 questions by 1,102 students in 36 classes. 
Nineteen is a suspiciously high number of factors, and 108 
questions is a high number of questions to expect students 
to answer conscientiously. 



- 58 - 

High outliers . The characteristics of the studies 
producing high outliers are given in Table 4-2. The points 
in common with the studies is that they were published in a 
journal and teacher clarity was rated by college students. 



Table 4-2. Characteristics of Studies Producing High Outliers 



ID VER 


PUB 


STU 


ACH REL 


TEX 


WKS 


GRA 


NS 


NC 


DIM 


R 


SMR 


SMZ 


11 1 


1 


1 


2 . 


-1 


15 


13 


26 


17 


EXP 


.81 


.46 


.50 


43*-l 


1 


1 


4 . 


1 


15 


13 


78 


20 


ORG 


.77 


.73 


.93 


44 -1 


1 


1 


3 . 


1 


10 


13 


35 


13 


EXP 


.79 


.73 


.93 



Note. To interpret the heading abbreviations see the note 
below Table 4-1. 

* The same students rated all 10 teachers in 20 subject areas. 



Reliability of Dimensions of Teacher Clarity 

Four teachers completed the task of classifying teacher 
behaviors into the dimensions of teacher clarity. The 
percentage of agreement with my classifications varied from 
64% to 93% with a mean of 76%. Many measures only achieve a 
reliability of about .8, so I judged this value to be high 
enough to consider the classification as reasonably 
reliable. Most disagreement occurred in classifying factors 
(comprising a number of different teacher behaviors) as 
either SKI (more than one dimension) or as being dominated 
by a particular dimension. 
Characteristics of the Reduced Data Set 

The characteristics of the data set are sho^m in Table 
4-3. The prototypical study (a) was conducted in the 



- 59 - 



Table 4-3. Characteristics of Reduced Data Set 



Characteristic (Total number of _r = 100) N 



Educational setting 

Elementary school (Grades 1 - 6) 48 

Secondary school (Grades 7 - 12) 15 

College (Grade 13) 37 



Decade published 

60s 5 

70s 78 

80s 17 



Number of students in class — NS (Mean = 27) 

Large NS (30 and above) 42 

Small NS (less than 30) 58 



Number of classes in study — NC 

(Mean = 41. Total = 98-- 2 values not known) 

Large NC (40 and above) 35 

Small NC (less than 40) 63 



Number of Normal classes (course lasting at least 6 weeks 

with the regular teacher) 

Normal 80 

Experimental (sometimes just one lesson) 20 



Verbal or numerical ability 

Verbal 48 

Numerical 47 

Both 5 



Studies Published or not (not = ERIC or dissertation) 

Published 81 

Not published 19 



Teacher-clarity raters 

Students 40 

Observers 60 



continued 



- 60 - 



Table 4-3 continued 

Characteristic (Total number of r = 100) N 

Validity of comparison of class achievement gain 

Posttest only with essay test graded by class teacher 

(coded 0) 1 

Posttest only (no random entry of students to class) 

(coded 1) ^ 

Simple gain (posttest - pretest) 

(coded 2) ; : ^2 

Correlations reported for both simple gain 
and residual gain 

(coded 2.5) 3 

Residual gain (difference between actual and 
expected gain) 

(coded 3) 

Evidence of random entry of students to classes or same 
students rating different teachers 

(coded 4) ^ 

Validity not known 22 

Number of studies reporting reliability of teacher clarity 
(Total number = 24. Mean reliability = .78) 

Reliability approximately .5 2 

Reliability approximately .8 13 

Reliability approximately .9 9 

Experienced teachers or learners (learners = teaching 

assistants or student teachers) 

Experienced teachers 65 

Learners 28 

Both • ^ 

Number of weeks between start of teaching and 

the posttest (Mean =17 weeks) 

Less than 4 weeks 20 

4-11 weeks • ^ 

12-15 weeks 34 

16-27 weeks 3 

28 - 30 weeks 37 

Dimensions of teacher clarity 

Assessment of student learning — ASL 21 

Examples and guided practice — EGP 17 

Clarity of explanation — EXP 31 

Clarity of organization — ORG 21 

Clarity of speech — SP 4 

Overall rating of teacher clarity — SKI 6 



- 61 - 

elementary school or college rather than secondary school, 
(b) was published in the 1970s, (c) had 27 students per 
class and 41 classes per study, (d) was of a normal class 
(lasting a semester or a year) with the regular 
(experienced) teacher, anequally likely to depend on 
numerical ability as on verbal ability, (e) was more likely 
to use observers rather than students as raters of teacher 
clarity and the reliability of the rating was about .8, (f) 
was most likely to use a measure of achievement gain (simple 
or residual) rather than to use posttest only and/or random 
assignment of students to classes, and (g) investigated any 
of the four dimensions of teacher clarity (ASL, EGP, EXP, 
ORG) rather than the prerequisite of teacher clarity 
(clarity of speech — SP) or the overall rating of teacher 
clarity (SKI). 

Relationships Between Characteristics 

Correlations between the characteristics are shown in 
Table 4-4. The correlations between teacher clarity and 
student achievement gain (effect size — r_) are shown to 
increase with grade (higher in college than in school), when 
the studies are published in journals or books (rather than 
ERIC or dissertations), and when students do the rating 
rather than observers. Among those (24) effect sizes from 
studies that report the reliability of the teacher-clarity 
measure, the effect size increases as the reliability of the 
teacher-clarity measure increases. 



- 62 ~ 



Table 4-4. Correlations in Data Set 





**YR 


VER 


PUB 


STU 


ACH 
N=79 


REL 
N=24 


TEX 


WKS 


GRA 


NS NC 
N=98 




O O * 




















TIT TT> 

PUB 


o c 
ZD* 


-15 


















omr T 
b iU 


— uy 


00 


o tr * 

25* 
















A T r 


O C 4- 


-05 


-14 


-08 














KhLi 


Jo 


04 


/O* 


31 


-40* 












i bX 


Uo 


— i / 


1 O 

-io 


cr o * 

-53* 


33* 


-48* 












J. D 


— u 


9 1 * 


— Z Z^ 


UD 


"3 * 










GRA 


03 


05 


31* 


76* 


-14 


43* 


-61 * 








NS 


00 


-09 


09 


04 


26* 


-49* 


33* 


26* 


-09 




NC 


-20* 


22* 


-14 


17 


12 


-02 


-05 


00 


22* 


-06 


_r 


11 


-07 


35* 


27* 


-19 


66* 


-08 


-11 


31* 


13 -14 



Note . Decimal point omitted, N = 100 when not given. 

**YR = year study reported, VER = subject based on verbal 

ability, PUB = published in journal or book, STU = teacher 

clarity rating by students, ACH = validity of achievement 

gain, REL = reliability of teacher clarity measure, TEX = 

experienced teacher, WKS = weeks of course, GRA = grade, NS = 

number of students in class, NC = number of classes in study, 

X = correlation between teacher clarity and student 

achievement gain. 

*Significant (_£ = .05 or less). 



- 63 - 

Other significant correlations between variables show 

that 

1. More recent studies have (a) tended to study 
subjects based on the students* numerical ability rather 
than their verbal ability, (b) tended to be published in 
journals or books, (c) used measures of achievement gain 
that are assumed to be of lower validity, and (d) used fewer 
classes in the study. 

2. Studies of verbal subjects tend to use a higher 
number of classes in the study. 

3. Published studies tend to (a) use students as 
raters, (b) have a higher reliability of measurement of 
teacher clarity, and (c) be at the college level. (At the 
college level the courses are about 15 weeks. At school the 
courses are about 30 weeks. Thus, if the majority of 
published studies are at the college level (Grade 13) there 
will be a negative correlation between PUB and WKS. There 
is also a positive correlation between PUB and GRA. These 
two correlations indicate that the study was at the college 
level . ) 

4. Students are more likely to do the teacher-clarity 
rating (a) when the teachers are learners and (b) at the 
college level. 

5. The assumed validity of the measure of achievement 
gain tends to be (a) negatively related to the reliability 
of the measure of teacher clarity, (b) be higher when the 
teachers are experienced than when they are learners, and 



- 64 - 

(c) be higher in classes with a large number of students. 

6. The reported reliability (only 24 studies) is 
higher (a) when the teachers are learners, (b) at the 
college level, and (c) when the number of students in the 
class is low. 

7. Experienced teachers tend to (a) be studied in 
school rather than college and (b) have a larger number of 
students than learner teachers. 

8. The length of a course is (a) longer at school 
than at college and (b) longer when the number of students 
in the class is higher (normal classes tend to have a 
higher number of students than experimental classes that 
sometimes consist of only one lecture). 

9. At higher grade levels the number of classes used 
in the study tends to be larger. 

Glass Analysis 

The data used in the analyses are shown in Table A- 2 in 
Appendix A {R_ in the table is referred to as r in the 
analyses as is used as the mean). In the following 
analysis the methods of Glass et al. (1981) were used. 
Treating Each Effect Size as Independent 

The unweighted mean for the 100 values of r_ in the 
reduced data set (i.e., after the removal of the outliers) 
was .30 with a standard deviation of .19. The standard 
error (standard deviation of the mean — standard deviation 
divided by the sguare root of the number of values) was 



- 65 - 

therefore .02 and the uncertainty in the mean is twice this 
value, .04. The 95% confidence interval of the population 
value of the mean correlation between all dimensions of 
teacher clarity and the achievement gain of the students is 
therefore between .25 and .34. 

Glass does not recommend correcting for the 
unreliability of the teacher clarity measure, but in order 
to make a comparison with the results by Hunter's method and 
Hedges 's method the correction was made. The mean 
reliability of the teacher clarity measure is .78. The 
square root of this is .88. Dividing the original limits of 
the confidence interval by this value, the population 
correlation is estimated to be between .30 and .39. 

What was the effect of dropping the outliers? The mean 
of all 110 effect sizes was .28 with a standard deviation of 
.25. This gives a standard error of .25 divided by the 
square root of 118, that is .024, and an uncertainty of .05. 
The confidence interval of the population mean using the 
full data set is between .23 and .33. Correcting for 
unreliability in the teacher clarity measure, it is between 
.27 and .38. This does not vary appreciably from the 
confidence interval obtained using the reduced data set. 
Using Tukey's Jackknife Method 

In this method, pseudo-values of r for each study are 
calculated by (a) obtaining the mean value of r from all 38 
studies (using all the values of r) and multiplying this 
mean by 38; (b) obtaining 38 means of r from 38 studies, 



- 66 - 

dropping all the effect sizes from one study at a time, and 
multiplying each mean by 37; and (c) subtracting each result 
in (b) from the value obtained in (a) in order to obtain 38 
pseudo- values that represent the effect of dropping each 
study from the data set. The population confidence interval 
is then estimated from the mean and uncertainty in the mean 
calculated using these pseudo-values. 

The value for (a) was found to be 38 x .29990 = 11.396 
and the values for (b) and (c) were as shown in Table 4.5. 
This leads to a mean of .31 and an uncertainty of .04 so the 
population mean is estimated to be between .27 and .35. 
Correcting for the unreliability in the measure of teacher 
clarity, this becomes .31 through .40. Thus, using the 
jackknife method merely raises the confidence interval by 
.01 above the confidence interval obtained from using the 
original effect sizes. 
Regression Equations 

Model . The model used in the regression equation was 
_r = YR VER PUB STU ACH TEX WKS GRA NS X G 0 A S 
where x = correlation between teacher clarity and 
achievement gain 
YR = year of report 
VER = subject based on verbal ability 
PUB = published in book or journal 
STU = student rating of teacher clarity 
ACH = validity of comparison of achievement gain 
TEX = Experienced teacher 



- 67 - 

Table 4-5. Tukey's Jackknife Method for Determining 



the Confidence Interval of the Effect Sizes 



ID 



Values of r 



( Mean ) 


(b)* 


(c)** 


. (.30) 


11.248 


.312 


. ( .38) 


10.915 


.645 


. ( .32) 


11.248 


.312 




11.386 


.174 




11.174 


.386 


. ( .20) 


11.285 


.275 


. ( .31) 


11.248 


.312 


. ( .26) 


11.359 


.201 




11.433 


.127 




11.211 


.349 




11.211 


.349 




11.285 


.275 




11.036 


.524 




11.248 


.312 




11. 322 


.238 




11.137 


.423 




11.248 


.312 




11.174 


.386 




11.285 


.275 




11.359 


.201 




11.285 


.275 




11.507 


.053 


(-.04) 


11.322 


.238 




11.396 


.164 




11.174 


.386 


. ( . 12) 


11.692 


.132 




11.174 


.386 




10.989 


.571 




11.322 


.238 


(-.03) 


11.507 


.053 




11.248 


.312 


. ( . 11) 


11.211 


.349 




11.211 


.349 




11.470 


.090 




11.211 


.349 




11.100 


.460 




11.137 


.423 




10.952 


.608 



1 

2 

3 
4 

5 . 

6 . 

7 . 

8 . 

9 . 

10 . 

11 . 

12 . 

13 . 

14 . 

15 . 

16 . 

17 . 

18 . 

19 . 

20 . 

21 . 

22 . 

23 -. 
27 . 

29 . 

30 . 

31 . 

32 . 

33 . 

34 -. 

35 . 
37 . 

39 . 

40 . 

41 . 

43 . 

44 . 

45 . 



46 
40 
34 
30 
07 
37 
20 
31 
36 
08 
30 
42 
34 
59 
22 
14 
47 
10 
58 
41 
07 
26 
07 
19 
30 
49 
20 
54 
68 
11 
03 
30 
07 
47 
14 
43 
69 
67 
68 



.06 .38 

,45 .22 .32 .49 .53 .27 .50 
,32 .06 .42 .33 .44 .17 .37 

,33 

,13 

.39 .34 



.15 
.30 
.48 
.21 
.18 
.29 
.40 



,10 
,40 



35 
36 



.51 
.61 



21 



.21 

53 .01 



32 
11 
11 

24 



02 
61 

-.03 ... 

33 

,13 

01 .14 



.32 .67 



-.06 .44 , 

,00 

,27 .18 .17 -.14 .04 .19 



Mean .31 
SD .14 
SE .02 



Note , (a) = 38 X mean of all 38 values = 38 x .30421 = 11.560 
*(b) = 37 X mean of all values except that study 
**(c) = (a) - (b) 



- 68 - 



WKS 




Length of course 








GRA 




Grade (college = 


13) 






NS 


— 


Number of students in class 




X 




1 when dimension 


of 


teacher clarity = 


EXP 


G 




1 when dimension 


of 


teacher clarity = 


EGP 


0 




1 when dimension 


of 


teacher clarity = 


ORG 


A 




1 when dimension 


of 


teacher clarity = 


ASL 


S 




1 when dimension 


of 


teacher clarity = 


SP 



and X, G, 0, A, S = - 1 when dimension = SKI. 
Only 76 values of jr were used because of missing values. 
The mean of these 76 values was .29 with an uncertainty of 
.04. Thus the mean of the 76 values is only .01 less than 
that obtained earlier with 100 values. The model accounted 
for 46% of the variance in _r« 

Significant variables . The variables with significant 
(.05) Type I sums of squares (assumes variable is entered in 
the order given in the equation) were PUB, STU, and GRA. 
These variables were also significantly correlated with the 
effect size (Table 4-4). The variables with significant 
Type III sums of squares (assumes variable is entered in 
last in the equation — which is the same as testing the 
significance of the regression coefficient) were ACH 
(negative), TEX, and GRA. 

The fact that PUB and STU were no longer significant 
when entered last indicates that the difference in effect 
size reported in published studies (or those with student 
raters) compared to those reported in unpublished studies 



- 69 - 

(or with observer raters) was due to the correlations 
between PUB (STU) and other variables. For example. Table 
4-4 shows that published studies correlate with grade .31 
(STU with GRA .75) and that grade correlates with effect 
size .31. Thus, the higher effect size in published studies 
is due in part to the fact that higher effect sizes are 
obtained in a college setting (see Table 4-8) and that a 
higher percentage of published studies are at the college 
level rather than the elementary school level. 

Varying the model. The model was reduced by removing 
the dimensions of teacher clarity. This reduced the 
variance in r accounted for by 9% to 37%. This difference 
is not significant. The only significant regression 
coefficient was GRA. ACH (p = .08) and TEX (p = .057) were 
nearly significant. PUB (p = .89) was far from significant. 

The number of classes (NC) used in obtaining the value 
of r was added to the previous model. This increased the 
variance accounted for by less than 1%, and the regression 
coefficient was not significant. Thus, the number of 
classes in the study was not a meaningful source of 
variance. GRA was still the only significant regression 
coefficient. 

Only 76 effect sizes were used in the above analyses 
because of 22 misssing values of ACH (16 in Berliner & 
Tikunoff, 1977, 3 in Bryson, 1974, and 3 in Bourke, 1985) 
and 2 missing values of NS (Centra, 1977). The missing 
values Of ACH were set at 2.5 (the median value), and the 



- 70 - 

original model was then run with 98 observations. The mean 
Of the 98 is .30, which is only .01 more than that obtained 
earlier with 76 values. The model accounted for 36% of the 
variance in r instead of the 46% accounted for with 76 
observations. The variables with significant (.05) Type I 
sums of squares were still pub, stu, and GRA. The variables 
with significant Type III sums of squares (and regression 
coefficients) were ACH (negative), TEX, and PUB. Note that 
with the addition of the 22 effect sizes PUB becomes 
significant and GRA is no longer significant. 

When NO (number of classes that contributed to the 
effect size) was added to the 98-observations model, the 
variance in r accounted for increased by 3% to 39%. The 
significant regression coefficients with this model were 
ACH, TEX, and PUB as in the model without NC, but also 
included GRA and NC. 

Summary. The significant regression coefficients with 
the various models and observations were as follows: 

1. When 76 observations and the teacher clarity 
dimensions were used, ACH (the validity of comparison of 
achievement gain between classes) was negative and 
significant. TEX (experienced teacher) and GRA (grade) were 
positive and significant. 

2. With 76 observations and no teacher clarity 
dimensions only GRA significant and it was positive. 
There was no significant change when NC (number of classes 
contributing to the effect size) was added to the model. 



- 71 - 

3. When 98 observations and the teacher clarity 
dimensions were used, ACH (negative), TEX, and PUB 
(published in journal or book) were significant. GRA 
(p = .14) was not significant. 

4. When NC was added to the model in 3, NC and GRA 

(£ = .03) were added to the significant variables: ACH, TEX, 
and PUB. 

I will report how these results compare with those 
obtained by the Hunter and Hedges methods before analyzing 
by subsets. 

Hunter Analysis 
The methods of Hunter et al. (1982) were used in the 
analysis that follows. 

The Weighted Mean Effect Size from Each Study 
Calculations . 

K = number of studies = 38 
N = total number of classes = 1699 
Observed variance = .0325 

2 2 

Estimated sampling variance = (1 - R ) x K/N 

= (1 - .30^)^ X 38/1699 

= .0185 

Estimated population variance = .0325 - .0185 

= .0140 



Estimated population SD = .12 
jpulation SE = .12 
Uncertainty = . 04 



1/2 

Estimated population SE = .12/38 - .02 



- 72 - 

95% confidence interval of population mean 

= .30 plus or minus .04 

= .26 => .34 

1/2 

Corrected mean = .30/. 78 = .34 

Corrected confidence interval 

= .30 => .39 

Test of homogeneity: 
Observed variance x N = .0325 x 1699 

= 55.22 

2 2 

Expected variance = ( 1 - R ) 

2 2 

= (1 - .30 ) = .8281 
Test statistic = 55. 22/. 8281 = 66.7 
Chi-square with 37 degrees of freedom = 52 
Therefore the effect sizes are not homogeneous. 

Commentary . The values of _r are weighted by the number 
of classes which contribute to the correlation on the 
assumption that the larger the number of classes the more 
valid the value. The weighted mean for the 38 mean values 
of _r in each study is .30 with a standard deviation of .18 
as shown in the calculations. The observed variance ( SD 
squared) is .0325. The estimated variance due to sampling 
error is .0185, leaving an estimated population variance of 
.0140. This gives a population standard deviation of .12, a 
standard error of .02, and an uncertainty in the mean of 
.04. The 95% confidence interval of the population value of 
the mean correlation between all dimensions of teacher 
clarity and the achievement gain of the students is 



therefore between .26 and .34. This is exactly the same 
interval that was obtained using the Glass method. 
Correcting for the unreliability of teacher clarity results 
in an interval of .30 through .39. 

TO test the uncorrected result for homogeneity, the 
test statistic (ratio of the observed variance to the 
expected variance if all sample correlations were estimates 
of a single population correlation) is compared to the .05 
value of chi-square using the number of studies minus one 
(37) as the number of degrees of freedom. The test 
statistic (66.7) is greater than the chi-square value (52) 
so the effect sizes are not homogeneous, and it is therefore 
necessary to use regression equations and analyze by 
subsets. 

Reg ression Equations 

The model used in the regression equation was 
r = YR VER PUB STU ACH TEX WKS GRA NS X G 0 A S 
where the variables are the same as in the Glass method but 
the effect size (r) is weighted by the value of NO (the 
number of classes contributing to r) on the assumption that 
the larger the number of classes the more valid the value. 
The significant variables were determined by using 
generalized least squares implemented on the WEIGHT = NC 
option of SAS GLM. 

The model accounted for 54% of the variance in _r 
compared to 46% using the Glass unweighted method. Setting 
the missing values of ACH to 2.5 and using 98 observations 



- 74 - 

reduced the variance accounted for to 43%. The variables 
with significant regression coefficients in the model with 
76 observations were ACH (negative), TEX, and GRA. With 98 
observations, GRA (p = .42) was no longer significant; ACH 
and TEX remained significant. PUB was not significant in 
either case. 

Hedges Analysis 
The methods of Hedges and Olkin (1985) and Hedges 
(1988) were used in the following analysis. 
The Weighted Mean Effect Size From Each Study 
Hedges and Olkin (1985) calcu lations. 
K = number of studies = 38 
N = total number of classes = 1699 
Observed variance = .0421 
Mean ^ = . 33 (Mean R = .32) 
Estimated population variance in Z_ = 1/(N - 3K) = 1/1585 

= 1/(N - 3K) = 1/1585 
= .0066 

Estimated population SE = .0066 ^ = .025 
Uncertainty = .05 
95% confidence interval of population mean Z. 

= .33 plus or minus .05 
= .28 => .38 
(R = .27 => .36) 
Corrected mean ^ = .33/. 88 = .38 
Corrected mean R. = .35 
Corrected confidence interval of Z_ 

= .32 => .43 (R = .29 => .41) 



- 75 - 

Hedges and Olkin (1985) commentary . The weights used 
are NZ where NZ = NC - 3 as that is the number of degrees of 
freedom in z when the class is the unit of analysis. The 
weighted mean for the 38 mean values of z. in each study is 
.33 ( r. = .32) . The estimated standard error is .025 giving 
an uncertainty of .05. The 95% confidence interval for 
population mean of is from .28 through .38 (r = .27 
through .36). This compares with .26 through .34 obtained 
by the previous two methods. 

Hedges (1988) calculations . 

K = number of studies = 38 
N = total number of classes = 1699 

Observed variance = .0421 
Estimated sampling variance = K/(N - 3K) 

= 38/1585 
= .0324 

Estimated population variance in _z = .0421 - .0324 

= .0181 

Estimated population SD = .0181''^ = .13 
Estimated population SE = .13/38^ = .022 
Uncertainty = .04 
95% confidence interval of population mean Z 

= .33 plus or minus .04 
= .29 => .37 
(R = .28 => .36) 
Corrected mean ^ = .38 (Mean R_ = .36) 
Corrected confidence interval -of Z 

= .33 => .42 ( R. = .32 => .40). 



- 76 - 

Hedges (1988) commentary . The mean of z_ is .33 with an 
observed variance of .0421. The estimated variance due to 
sampling error is .0324 leaving an estimated population 
variance of .0181. This gives a population standard 
deviation of .13, a standard error of .02, and an 
uncertainty in the mean of .04. The 95% confidence interval 
of the population value of the mean _z is therefore between 
.29 and .37, which corresponds to a confidence interval for 
the mean r_ of .28 through .36. This is practically the same 
interval that was obtained using all the other methods. 
Correcting for the unreliability of teacher clarity results 
in an interval of .32 through .40. 

Test of homogeneity . 

Test statistic = observed variance x (N - 3K) 
= .0421 X 1585 = 66.7 

Chi-square with 37 degrees of freedom = 52 
Therefore the effect sizes are not homogeneous. 

To test the uncorrected result for homogeneity the test 
statistic (ratio of the observed variance to the expected 
variance if all sample correlations were estimates of a 
single population correlation) is compared to the .05 value 
of chi squared using the number of studies minus one (37) as 
the number of degrees of freedom. The test statistic (66.7) 
is greater than the chi-squared value (52) so the effect 
sizes are not homogeneous, and it is therefore necessary to 
use regression equations and analyse by subsets. 



- 77 - 

Regression Equations 

The model used in the regression equation was 
z_ = YR VER PUB STU ACH TEX WKS GRA NS X G 0 A S 
where _r = correlation between teacher clarity and 
achievement gain 
z = .5 X log( (1 + r)/(l - r) ) 
YR = year of report 
VER = subject based on verbal ability 
PUB = published in book or journal 
STU = student rating of teacher clarity 
ACH = validity of comparison of achievement gain 
TEX = Experienced teacher 
WKS = Length of course 
GRA = Grade (college = 13) 
NS = Number of students in class 
X = 1 when dimension of teacher clarity = EXP 
G = 1 when dimension of teacher clarity = EGP 
0=1 when dimension of teacher clarity = ORG 
A = 1 when dimension of teacher clarity = ASL 
S = 1 when dimension of teacher clarity = SP 
and X, G, 0, A, S=-l when dimension = SKI. 

Each value of _z_ was weighted by the value of NZ (the 
number of classes contributing to _r minus three, that is, 
the number of degrees of freedom in _z) . The significant 
variables were determined by using generalized least squares 
implemented by the WEIGHT = NZ option of SAS GLM. The 
number of observations used was 76 out of the ICQ due to 



- 78 - 

missing values of ACH and NS. The model accounted for 57% 
of the variance in r compared to 54% using the Hunter method 
and 46% using the Glass unweighted method. Setting the 
missing values of ACH to 2.5 and using 98 observations 
reduced the variance accounted for was to 46%. 

The variables with significant regression coefficients 
in the model with 76 observations were ACH (negative), TEX, 
and GRA. IVith 98 observations, GRA (p = .34) was no longer 
significant; ACH and TEX remained significant. PUB was not 
significant in either case. 

Comparison of Results Using Different Methods of Analysis 
Confidence Intervals 

Table 4-6. Confidence Intervals of All Effect Sizes 

Using Different Methods 

Uncorrected Corrected* 
Method (N = 100, K = 38)** Mean Interval Mean Interval 



Glass et al. (1981) 
Mean of 100 values of r 
Unweighted mean 
Tukey's Jackknife 
Mean of 38 pseudo 
study values of r 



.30 .26--. 34 .34 .30--. 39 
.31 .27— .35 .35 .31--. 40 



Hunter et al. (1982) 
Mean of 38 study values of r 

Variance = observed - formula .30 .26 — .34 .34 .30 — .39 

Hedges & Olkin (1985) 
Mean of 38 study values of z. 

Variance by formula .32 .27 — .36 .36 .31 — .40 

Hedges (1988) 
Mean of 38 study values of _z 

Variance = observed - formula .32 .28 — .36 .36 .32 — .40 



Note . *Corrected for unreliability in teacher clarity. 
**N = number of effect sizes, K = number of studies. 



r 79 - 

Table 4-6 shows that all the different methods 
essentially give the same confidence interval for the effect 
size. 

Regression Equations 

Table 4-7 shows the results of using different models, 
different numbers of observations, and different weights in 
the regression analyses. 



Table 4-7. Regression Equation Results 
Using Different iMethods 

Model Weight N R^% sig. Reg. Coeff. 

Glass et al. (1981) ' ' ' • ' • • """"^ " 

r = YR— S 1 76 46 ACH(-) TEX GRA 

r = YR — NS No TC* 1 75 37 GRA 

j: = YR— NS+NC No TC 1 76 37 GRA 

r = YR— S 1 98 36 ACH(-) TEX PUB 

r = YR— S +NC 1 98 39 ACH(-) TEX GRA PUB NC 

Hunter et al. (1982) 

r = YR—S NC 76 54 ACH(-) TEX GRA 

r = YR—S NC 98 43 ACH(-) TEX 

Hedges & Olkin (1985) 

z = YR—S NC-3 76 57 ACH(-) TEX GRA 

z = YR—S NC~3 98 46 ACH(-) TEX 

Note. *TC = the dimensions of teacher clarity 



Significant regression coefficients . ACH (the validity 
of the comparison of achievement gain) and TEX (experienced 
teacher) were significant in all cases except when the 
dimensions of teacher clarity were dropped from the model. 
Thus we can be fairly certain that (a) the correlation 
between teacher clarity and achievement gain (i.e., effect 
size) decreases as the validity of the achievement gain 
comparison increases, and (b) the effect is greater with 



- 80 - 

experienced teachers than it is with learners (student 
teachers and assistant teachers). 

GRA (grade) is significant most times: It is likely 
that teacher clarity is more important at the higher grades 
than it is in the first years of elementary school. PUB 
(study published in a journal or book) is significant only 
in Glass analysis with 98 observations so the large 
difference in the mean of jc for published studies--. 38 and 
the mean for unpublished studies--. 21 (see Table 4-8) is 
largely due to the fairly high correlation between PUB and 
such variables as GRA and STU (student raters) which are 
positively correlated with the effect size (see Table 4-4). 

Analysis of Subsets 

The methods of Glass et al. (1981)--GL, Hunter et al. 
(1982) — HU, Hedges and Olkin (1985)— HO, and Hedges 
(1988): — HE were used in the following analyses. Refer to 
the values corrected for unreliability in the measure of 
teacher clarity in Table 4-8. 

Educational Setting — GRA . The overall mean effect 
sizes using the means obtained in all analyses (HO and HE 
count as one result as these analyses obtain the mean by the 
same method) are (a) elementary school (Grades 1-6) .26, 
(b) secondary school (Grades 7 - 12) .30, and (c) college 
(Grade 13) .42. All analyses except for HO find the 
difference between elementary school and college significant 
(the confidence intervals do not overlap). HE also finds a 
significant difference between secondary school and college 
(For HU this difference is nearly significant as the 



- 81 - 

Table 4-8. Confidence Intervals of subsets of Effect Sizes 

Uncorrected Corrected** 

Characteristic N Anal* Mean Interval Mean Interval 

Educational setting 

Elementary school ... 48 GL .25 .20— .30 .28 .23--. 34 

HU .22 .19— .25 .25 .22— .28 

HO .22 .18— .26 .25 .20— .39 

HE .22 .18— .26 .25 .20— .39 

secondary school 15 GL .30 .21 — .39 .34 .24— .44 

HU .25 .21— .29 .28 .24— .33 

HO .25 .21— .29 .28 .24— .33 

HE .25 .21 — .29 .28 .24— .33 

Colleqe 37 GL .38 .32— .44 .43 .36— .50 

HU .35 .29— .41 .40 .33— .47 

HO .35 .29— .41 .40 .33— .47 

HE .35 .29— .41 .40 .33— .47 

Decade pub lished 

— 50i 5 GL .37 .14— .60 .42 .16— .68 

HU .29 .14— .44 .33 .16— .50 

HO .29 .14— .44 .33 .16— .50 

HE .29 .14— .44 .33 .16— .50 

70s 78 GL .30 .26— .34 .34 .30— .39 

HU .29 .25— .33 .33 .28— .38 

HU .29 .25— .33 .33 .28— .38 

HU .29 .25— .33 .33 .28— .38 

80s 17 GL .32 .22— .42 .36 .25— .48 

HU .28 .24— .32 .32 .27— .36 

HO .29 .24— .32 .33 .27— .36 

HE .29 .24— .32 .33 .27— .36 

Studies Published or not 
(not = ERIC or 
dissertation) 

Published 81 GL .33 .29— .37 .38 .33— .42 

HU .32 .28— .36 .36 .32— .41 

HO .32 .28— .36 .36 .32— .41 

HE . 32 . 28— . 36 . 36 .32— .41 

Not published 19 GL .19 .13— .25 .22 .15— .28 

HU .18 .14— .22 .20 .16— .25 

HO .18 .14— .22 .20 .16— .25 

HE .18 .14— .22 .20 .16— .25 



continued 



Table 4-8 continued 



- 82 - 



Uncorrected Corrected 
Characteristic N Anal* Mean Interval Mean Interval 



Number of students in class — NS 



(Mean = 27) 


















Large NS (30 and above) 


42 




34 


, 26 


. 38 


. 39 


. 30 — 


. 43 




HU 


. 39 


. 35 — 


.43 


. 44 


. 34 — 


.49 






HO 


. 39 


. 35 — 


. 43 


.44 


. 34 — 


.49 






HE 


. 39 


. 35 — 


.43 


.44 


. 34 — 


.49 


Small NS (less than 30) 


58 


GL 


. 28 


. 23 — 


. 33 


. 32 


. 26 — 


. 38 






HU 


. 23 


. 19 — 


. 27 


. 26 


. 22 — 


. 31 






HO 


.23 


.19— 


.27 


.26 


.22— 


.31 






HE 


.23 


.19— 


.27 


. 26 


.22— 


.31 


Number of classes in study-- 


-NC 














(Mean = 41) 


















Large NC (40 and above) 


35 


GL 


. 29 


. 23 — 


. 35 


. 33 


. 26 — 


. 40 




HU 


. 27 


. 22 — 


.32 


. 31 


. 25 — 


. 36 






HO 


27 


. 22 


. 32 


. 31 


. 25 


. 36 






HF 


27 


22 




- 31 


25 


. 36 


Small NC (less than 40) 


63 


GT. 


31 


26 


36 


. 35 


30 


41 






HU 


. 30 


. 27 — 


. 33 


. 34 


.31 


. 38 






HO 


30 


26 


36 


34 


30 


41 

• T X 








30 


26 


36 


34 


30 


4 1 


Normal classes — NOR 


















(course lasting at least 
















6 weeks with the regular 
















teacher) 




















80 


GL 


.30 


. 26-- 


34 


34 


30 


3Q 






HU 


. 28 


. 24 


32 


3? 


27 


36 






HO 


. 30 


26 


34 












HE 


. 30 


26 


34 


34 




• -j^ 




20 


GL 


.31 


22 


40 


35 










HU 


.30 


. 25— 


. 35 


.34 


.28— 


.40 






HO 


.31 


. 22 — 


.40 


. 35 


. 25 — 


.45 






HE 


. 31 


.22-- 


.40 


.35 


.25— 


.45 


Verbal or numerical ability- 


--VER 














48 


GL 


.34 


.29— 


.39 


.39 


,33— 


.44 






HU 


.29 


.25— 


.34 


.33 


.28— 


.39 






HO 


.29 


.25— 


.34 


.33 


.28— 


.39 






HE 


.29 


.25— 


.34 


.33 


.28— 


.39 




47 


GL 


. 30 


.25— 


.35 


.34 


.28— 


.40 






HU 


.31 


.27— 


.35 


.35 


.31 — 


.40 






HO 


.31 


.27— 


.35 


.35 


.31 — 


.40 






HE 


. 31 


. 27— 


.35 


.35 


.31 — 


.40 



continued 



Table 4-8 continued 



- 83 - 



Uncorrected Corrected 



Characteristic 


N Anal* 


Mean 


Interval 


Mean 


Interval 


Validity of comparison 


of class 


achievement gain — ACH 




Posttest only 


















(no random entry of 


9 


GL 


.49 


. 38— 


.60 


.56 


.43— 


. 58 


students to class) 




T TT T 

HU 


. b4 


. 3 i 


. D / 


. b 1 


. Do 


.65 


(coded 1) 




HO 


.55 


.45 — 


.62 


.63 


.51 — 


.70 






HE 


.55 


.52— 


.57 


.63 


.59— 


.65 


Simple gain 


















(posttest - pretest) 


















(coded 2) 


1 1 


GL 


. JU 


. 15 — 


. J / 




. ZD 


.42 






HU 


.29 


. 26— 


.32 


.33 


.30— 


.36 






HO 


.29 


.16— 


.42 


.33 


.18— 


.48 






HE 


.29 


.26— 


.32 


.33 


.30— 


. 35 


Residual gain 


















(difference between 


45 


GL 


.25 


.19— 


.31 


.28 


.22— 


. oD 


actual and expected 




HU 




. i y — 


. 


. ZD 


. £.2. 


. 28 


gain) 




HO 


.22 


.17— 


.27 


.25 


.19— 


.31 


(coded 3) 




HE 


.22 


.19— 


.25 


.25 


.22— 


.28 


Evidence of random entry 
















of students to 


8 


GT, 


. 37 


20 


54 


42 


23 


. 51 


classes or same 




HU 


.35 


.25— 


.45 


.40 


.28— 


.51 


students rating 




HO 


. 36 


.16— 


.62 


.41 


.18— 


.70 


different teachers 




HE 


.36 


.29— 


.41 


.41 


.33— 


.47 


(coded 4) 


















Validity not known 


22 


GL 


.33 


.27— 


.39 


. 38 


.31 — 


.44 






HU 


.29 


. 26— 


.32 


. 33 


.30— 


. 36 






HO 


.29 


.20— 


. 38 


.33 


.23— 


.43 






HE 


.29 


.26— 


.32 


.33 


.30— 


.36 



Experienced teachers learners — TEX 
(learners = teaching assistants 
or student teachers) 



Experienced teachers 


65 


GL 


.29 


. 25— 


-.31 


. 33 


.28— 


-.35 






HU 


.27 


.23— 


-.31 


.31 


.26— 


-.35 






HO 


. 28 


.24— 


-.30 


.32 


.27— 


-.34 






HE 


.28 


.25— 


-.31 


.32 


.27— 


-.35 




28 


GL 


.32 


. 25— 


-.39 


.36 


.28— 


-.44 






HU 


. 25 


.21 — 


-.29 


. 28 


.24— 


-.33 






HO 


.25 


.18— 


-.32 


.28 


.20— 


-.36 






HE 


.25 


.20— 


-.30 


.28 


.23— 


-.34 




7 


GL 


.36 


.19— 


-.53 


.41 


.22— 


-.60 






HU 


.43 


.32— 


-.55 


.49 


.36— 


-.63 






HO 


.45 


.36— 


-.54 


.51 


.41 — 


-.61 






HE 


.45 


.36— 


-.53 


.51 


.41 — 


-.60 



continued 



Table 4-8 continued 



- 84 - 



Uncorrected Corrected 
Characteristic N Anal* Mean Interval Mean Interval 



Teacher-clarity raters — STU 

Students 40 GL .36 .30 — .43 .40 .33--. 48 

HU .34 .29--. 39 .39 .33--. 44 

HO .36 .31--. 41 .41 .35— .47 

HE .36 .30— .42 .41 .34— .48 







.27 


.22— 


-.32 


.31 


.25— 


-.36 




HU 


.24 


.20— 


-.28 


.27 


.23— 


-.32 




HO 


. 24 


.19— 


-.29 


.27 


.22— 


-.33 




HE 


.24 


.21 — 


-.27 


.27 


.24— 


-.31 



Dimensions of teacher clarity 







GL 


.26 


.18— .34 


.30 


.21— .39 






HU 


. 29 


. 23 — . 35 


.33 


. 26 — . 40 






HO 


. 30 


.22— .38 


.34 


. 25— .43 






HE 


.30 


.23— .36 


.34 


.26— .41 




, 17 


GL 


. 22 


.14— .28 


.25 


.16— .32 






HU 


.19 


.16— .22 


.22 


.18— .25 






HO 


.20 


.13— .27 


. 23 


.15— .31 






HE 


. 20 


.17— .23 


.23 


.19— .26 






GL 


.33 


.27— .39 


.38 


.31— .44 






HU 


.29 


.24--. 34 


.33 


.27— .39 






HO 


. 30 


. 21 — . 39 


. 34 


.24— .44 






HE 


.30 


.26— .34 


.34 


.30— .39 






GL 


.31 


. 23— .39 


.35 


.26— .44 






HU 


. 26 


.21 — .31 


. 30 


.24— .35 






HO 


. 26 


. 18— . 34 


.30 


.20— .39 






HE 


.26 


. 20— . 32 


. 30 


.24— .36 






GL 


.36 


.04— .68 


.41 


.05— ,77 






HU 


.39 


.13— .55 


.44 


.15— .63 






HO 


.40 


.21 — .59 


.45 


.24— .67 






HE 


.40 


.23— .46 


.45 


.26— .52 


Overall 


rating SKI. . 6 


GL 


.54 


.40— .68 


.61 


.45— .77 






HU 


.51 


.44— .58 


.58 


.50— .66 






HO 


.51 


.40— .62 


.58 


.45— .70 






HE 


.51 


.45— .57 


.58 


.51 — .65 



Note . **Corrected for unreliability in teacher clarity. 
*Method of analysis: GL = Glass, McGaw, & Smith, 1981; HU = 
Hunter, Schmidt, & Jackson, 1982; HO = Hedges & Olkin, 1985; 
HE = Hedges, 1988. 



- 85 - 

confidence intervals just touch at .33). The unweighted 
mean (GL) is higher than the weighted means (HU, HO, & HE) 
in all cases. 

Decade published — YR . There are no significant 
differences. The overall mean in all decades is .34. The 
only result worth noting is that the unweighted mean for the 
60s (GL = .42) obtained when there are very few results (5) 
is a long way from the overall mean. This suggests that 
using the weighted mean may produce more stable results than 
using the unweighted mean. 

Study published in journal or book — PUB . The overall 
mean for published studies is .37, and for ERIC documents 
and dissertions it is .2i. This difference is significant 
by all methods of analysis. 

Number of students in class — NS . The overall mean for 
large classes (30 or more students) is .32, and that for 
small classes .29. This difference is not significant by 
any method of analysis as the confidence intervals all have 
a range of about .12. 

Number of classes in the study — NC . The overall mean 
for large studies (40 or more classes) is .32 and that for 
small classes .34. This difference is not significant by 
any method of analysis. 

Normal classes — NOR . The overall mean for normal 
classes is .33 and that for experimental classes .35. This 
difference is negligible. 



- 86 - 

Verbal or numerical ability — VER . The overall mean for 
both verbal and numerical ability is about ,35. Note the 
value of .39 obtained using the unweighted mean (GL) even 
though there are 48 observations. This value is .06 above 
those obtained using weighted means, and might be another 
indication of the instability of the unweighted mean. 

Validity of comparison of class achievement gain — ACH . 
When only a posttest was given and there was no evidence of 
random assignment of students to classes, the overall mean 
effect size is .60. When simple gain (a difference score) 
is used, the mean is .33. When residual gain is used, the 
mean is .26. When there is evidence of random assignment of 
students to classes or the same students rate different 
teachers, the mean is .41. All methods of analysis show 
that the posttest-only studies result in a significantly 
higher value than do the gain-score studies. The HU and HE 
methods give the simple-gain studies a significantly higher 
mean than that for the residual-gain studies. The GL and HO 
methods give wide confidence intervals (.11 - .30) for both 
these sets of studies with the result that the difference in 
the means is not significant. There are only eight 
random-entry type effects, so the confidence interval using 
all methods is very wide. The high mean with posttest-only 
(coded 1) explains the negative regression coefficient 
obtained in the regression equations. 

Experienced teachers or learners — TEX . The overall 
mean with experienced teachers is .32 and that with learners 



- 87 - 

.31. So why was the regression coefficient significant? 
The explanation lies in the fact that TEX is correlated with 
GRA (grade) -.61, and GRA is correlated with effect size 
.31, which results in a small negative (-.08) correlation 
between TEX and effect size (in line with the slightly- 
smaller effect size for experienced teachers) when 
unweighted (GL) means are used. Thus, the significant 
regression coefficient indicates that there is closer 
relationship between teacher clarity and student achievement 
gain for experienced teachers than there is for learners 
after the effect of other variables (such as grade level) 
have been taken into account. 

Teacher-clarity raters — STU . When the rating of 
teacher clarity was made by students, the mean effect size 
was .40; by observers, it was .28. The two means were 
significantly different for all methods of analysis except 
for the Glass method. The main reason the regression 
coefficient for STU was not significant was the .76 
correlation between STU and GRA (student rating takes place 
mostly at the college level). Thus, after GRA has been 
entered into the eguation, there is very little independent 
variance due to STU. 

Dimensions of teacher clarity . The overall rating of 
teacher clarity (SKI, which includes at least two of the 
dimensions of teacher clarity) produced a significantly 
higher mean effect size (.60) than did the other dimensions. 
There were only four results for SP (clarity of speech) and 



- 88 - 

these were spread over a wide positive range so nothing can 
be said about this dimension — except that the correlation 
with achievement gain is positive. The HE (Hedges, 1988) 
method produced a narrower confidence interval than the 
other methods. Considering the other four dimensions, EGP 
(examples and guided practice) had the lowest effect 
size — .23, but this was not significantly different from 
that for the highest, EXP — .35. The other two dimensions 
ASL (assessment of student learning) and ORG (clarity of 
organization) both produced a mean value of .32. 

Differences Due to Method of Analysis 
Table 4-9 shows the differences between the effect size 
when a particular method is used and the average value using 
all methods of analysis. For example the first value, +2, 
for GL indicates that the mean for GL (.28 — see the 
corrected value of the mean in Table 4-8) is .02 higher than 
the average value ((.28 + .25 + .25)/3 = .26), using the 
values of the mean obtained from GL, HU, and HO. (In the 
case of the mean only, HE is not used in the average as the 
mean using HE is obtained in exactly the same way as the 
mean using HO.) The next three values for GL show that (a) 
the low end of the confidence interval is .02 higher than 
the average value using all methods, (b) the high end of the 
confidence interval is .01 lower than the average value, and 
(c) the width of the confidence interval is .03 narrower 
than the average value. 



- 89 - 



Table 4-9. Differences in the Last Digit in the Results 
in~Table 4-8 From Average Corrected Confidence 
Intervals Using the Four Methods of Analysis 



Characteristic N Anal* Mean Interval Width 



Educational setting 

Elementary school ... 48 



Secondary school .... 15 



College 37 



GL 


+ 2 


+ 2 — 


-1 


-3 


IIU 


-1 


+ 1 — 


-7 


-8 


HO 


-1 




+4 


+5 


HE 




-1~ 


+4 


+ 5 


GL 


+4 


0— 


+ 8 


+ 8 


HU 


-2 


0— 


-3 


-1 


HO 


-2 


0— 


-3 


-1 


HE 




0~ 


-3 


-1 


GL 


+ 2 


+ 2— 


+2 


0 


HU 


-1 


-1 — 


-1 


0 


HO 


-1 




-1 


0 


HE 




-1~ 


-1 


0 



Decade published 
60s 



70s 78 



80s 17 



Studies Published or not 
Published 81 



Not published 19 



GL 


+ 6 


0~ 


+ 13 


+7 


HU 


-3 


0~ 


-5 


-2 


HO 


-3 


0~ 


-5 


-2 


HE 




0— 


-5 


-2 


GL 


+ 1 


+1— 


+ 1 


0 


HU 


0 


-1-- 


0 


-1 


HU 


0 




0 


-1 


HU 




-1— 


0 


-1 


GL 


+ 2 


-1~ 


+9 


+ 10 


HU 


-2 


+1— 


-3 


-4 


HO 


-1 


+1— 


-3 


-4 


HE 




+1— 


-3 


-4 


GL 


+ 1 


+1— 


+ 1 


0 


HU 


-1 


0— 


0 


0 


HO 


-1 


0~ 


0 


0 


HE 




0~ 


0 


0 


GL 


+ 1 




+ 2 


+3 


HU 


-1 


0— 


-1 


-1 


HO 


-1 


0— 


-1 


-1 


HE 




0— 


-1 


-1 



continued 



- 90 



Table 4-9 continued 



Characteristic N Anal Mean Interval V/idth 



Number of students in class- 


— NS 










Large NS (30 and above) 42 


GL 


-3 


-3~ 


-5 


-2 


HU 


+ 2 


+ 1 — 


+ 1 


0 




HO 


+ 2 


+ 1 — 


+ 1 


0 




HE 




+ 1 — 


+ 1 


0 


Small NS (less than 30) 58 


GL 


+4 


+ 3— 


+ 5 


+2 




HU 


-2 




-2 


-1 




HO 


-2 




-2 


-1 




HE 




-1~ 


-2 


-1 


Number of classes in study- 


-NC 










Large NC (40 and above) 35 


GL 


+ 1 


+ 1 — 


+ 3 


+ 2 


HU 


-1 


0— 


-1 


-1 




HO 


-1 


0— 


-1 


-1 




HE 




0— 


-1 


-1 


Small NC (less than 40) 63 


GL 


+ 1 


0— 


+ 1 


+ 1 




HU 


-1 


+ 1 — 


-1 


-2 




HO 


-1 


0— 


+ 1 


+ 1 




HE 


-1 


0— 


+ 1 


+ 1 


Normal classes--NOR 














GL 


+ 1 


+ 1 — 


+ 1 


0 




HU 


-1 


-2— 


-2 


0 




HO 


+ 1 


+ 1 — 


+ 1 


0 




HE 




+ 1 — 


+ 1 


0 




GL 


0 


_1 — 


+ 1 


+2 




HU 


-1 


+ 2— 


-4 


-6 




HO 


0 




+ 1 


+2 




HE 




-1 — 


+ 1 


+ 2 


Verbal or numerical ability- 


— VER 












GL 


+4 


+4— 


+ 4 


0 




HU 


-2 


-1 — 


-1 


0 




HO 


-2 




-1 


0 




HE 






-1 


0 




GL 


-1 


-2— 


0 


+ 2 




HU 


0 


+ 1 — 


0 


-1 




HO 


0 


+ 1 — 


0 


-1 




HE 




+ 1 — 


0 


-1 



continued 



Table 4-9 continued 



- 91 



Characteristic 


N 


Anal 


Mean 


Interval 


Width 


Validity of comparison of 


class 


achievement 


gain-ACH 


Posttest only- 
















9 


GL 


-4 


-10— 


+ 1 


+ 11 






HU 


+ 1 


+ 5— 


-2 


-7 






HO 


+3 


-2— 


+ 3 


+5 






HE 




+6 


_2 


-8 


Simple gain 
















12 


GL 


+ 1 


0— 


+ 1 


+ 1 






HU 


0 


+4— 


-5 


-9 






HO 


0 


-8~ 


+7 


+ 15 






HE 




+4— 


-5 


-9 


Residual gain 
















45 


GL 


+ 2 


+ 1 — 


+4 


+ 3 






HU 


-1 


+ 1 — 


-3 


-4 






HO 


-1 


_2— 


0 


-2 






HE 




+ 1 — 


-3 


-4 


Evidence of random entry 


of students to 


classes 






GL 


+ 1 


-3~ 


+4 


+7 






HU 


-1 


+ 2— 


-6 


-8 






HO 


0 


-8- 


+ 13 


+ 21 






HE 




+ 7- 


-10 


-17 


Validity not known . . 


22 


GL 


+3 


+ 2— 


+4 


+2 






HU 


-2 


+ 1 — 


-4 


-5 






HO 


-2 


-6— 


+ 3 


+ 9 






HE 




+ 1 — 


-4 


-5 


Experienced teachers or 


learners — TEX 






Experienced teachers 


65 


GL 


+ 1 


+ 1 — 


0 


-1 






HU 


-1 


-1 — 


0 


+ 1 






HO 


0 


0— 


-1 


-1 






HE 




0~ 


0 


0 




28 


GL 


+ 5 


+4— 


+ 5 


+ 1 






HU 


-3 


0— 


-4 


-4 






HO 


-3 


_4__ 


-1 


+ 3 






HE 




-1 — 


-3 


-2 




7 


GL 


-6 


-13— 


-1 


-12 






HU 


+ 2 


+ 1 — 


+ 1 


0 






HO 


+4 


+ 6— 


0 


-6 






HE 




+6— 


-1 


-7 



continued 



- 92 - 



Table 4-9 continued 

Characteristic N Anal Mean Interval Width 

Teacher-clarity raters — STU 

Students 40 GL 0 -1— +1 +2 

HU -1 -1 3 -2 

HO +1 +1— 0 -1 

HE 0 — +1 +1 

Observers 60 GL +3 +1 — +3 +2 

HU -1 -1 1 0 

HO -1 -2 — 0 +2 

HE 0~ -2 -2 

Dimensions of teacher clarity 

ASL 21 GL -2 -4 2 +2 

HU +1 +1 — -1 -2 

HO +2 0~ +2 +2 

HE +1— 0 -1 



EGP 



17 



GL 
HU 
HO 
HE 



+ 2 
0 
-1 



-1- 
+ 1- 
-2- 
+ 2- 



+ 3 
-4 
+ 2 
-3 



+4 
-5 
+4 
-5 



EXP 



31 



GL 
HU 
HO 
HE 



+3 
-2 
-1 



+ 3— 

-1 — 
_4_. 

+ 2— 



+ 3 
-2 
+ 3 
-2 



0 
-1 
+7 
-4 



ORG 



21 



GL 
HU 
HO 
HE 



+ 3 
-2 
-2 



+ 2— 
0— 
_4_. 

0— 



+ 3 
-4 
0 
-3 



+ 1 
-4 
+4 
-3 



SP 



GL 
HU 
HO 
HE 



-2 
+ 1 
+ 2 



-13- +12 

-3 2 

+6— +2 
+ 8 13 



+ 25 
+ 1 
-4 

-21 



SKI 



GL 
HU 
HO 
HE 



+ 2 
-1 
-1 



-2- 
+ 3— 
-2— 
+ 3— 



+ 7 
-4 
0 
-5 



+9 
-7 
+2 
-8 



Note . *Method of analysis: GL = Glass, McGav, & Smith (1981); 
HU = Hunter, Schmidt, & Jackson (1982); HO = Hedges & Olkin 
(1985'; HE = Hedges (1988). 



- 93 - 

In Table 4-10 these values are averaged over the 32 
sets of results and the standard deviation is given. Thus 
for these sets of results the mean effect size using the GL 
method is on average .012 higher then the average mean 
effect size obtained using all methods of analysis. The 
standard deviation in the effect size is .026. This value 
multiplied by two and reduced to one significant figure is 
.05. The difference between the mean effect size obtained 
by the GL method and the mean effect size obtained by 
averaging the results from all methods therefore varies from 
about .01 - .05 = -.04 through .01 + .05 = .06 (the actual 
variation is from -.06 through .06). 

The differences in the mean due to method have the same 
magnitude (.01) for all methods. The GL method produces a 
mean (with these data) about .02 higher than that produced 
by the other methods. For example, if the GL method 
produces a mean effect size of .32, then the other methods 
are likely to produce .30. The variation (due to method) in 
the value of the mean by the GL method (+/- .05) is about 
twice as much as that produced by the other methods (.03 and 
.02) . 

The differences in the width of the confidence interval 
due to method have almost the same magnitude (.03) for all 
methods. The GL and HO methods produce a confidence 
interval about .06 wider than that produced by the HU and HE 
methods. This is because in the latter methods the 
estimated sampling variance is subtracted from the observed 



- 94 - 

variance. The variation (due to method) in the width of the 
confidence interval (+/- .11 and .09) is greater in the 
methods accumulating zs (HO and HE) than it is (+/- .06) in 
the methods accumulating rs (GL and HU). 



Table 4-10. Summary of Differences From Mean Corrected 
Confidence Intervals Using the Four Methods of Analysis 

N Anal* Mean From To Width 

Mean * 32 GL 1.2 -0.8 3.0 2.8 

Standard deviation (2.6)(1.3)(2.0)(2.8) 

Mean HU -0.5 0.4 -2.3 -2.6 

Standard deviation (1.3)(1.6)(2.0)(2.8) 

Mean HO -0.5 -1,1 0.8 1.9 

Standard deviation (1.1)(1.9)(3.8)(5.6) 

Mean of means HE -0.5 1.1 -2.0 -3.1 

Standard deviation ( 1. 1 ) ( 1. 1 ) ( 3.0) (4.6) 

Reducing to one significant figure and 
reintroducing the decimal point 

Mean 32 GL .01 -.01 .03 .03 

2 X Standard deviation ( . 05 ) ( . 03 ) ( . 04 ) ( , 06) 

Mean HU -.01 .00 -.02 -.03 

2 X Standard deviation ( . 03 ) ( . 03 ) ( . 04 ) ( . 06 ) 

Mean HO -.01 -.01 .01 .02 

2 X Standard deviation ( . 02 ) ( . 04) ( . 08 ) ( . 1 1 ) 

Mean HE -.01 .01 -.02 -.03 

2 X Standard deviation ( . 02 ) ( . 04) ( . 06 ) ( . 09 ) 

Note . "From" and "To" indicate the bottom and top 
limits of the 95% confidence interval. *Method of 
analysis: GL = Glass, McGaw, & Smith (1981); HU = 
Hunter, Schmidt, & Jackson (1982); HO = Hedges & 
Olkin (1985); HE = Hedges (1988). 



CHAPTER V 
CONCLUSIONS AND DISCUSSION 

It was assumed in this dissertation that the teacher's 

task is to assist as many as possible of her or his students 

to pass an examination at the conclusion of the course (with 

as high a score as possible). The objective of this 

dissertation was to determine the correlation between 

teacher clarity of communication and the achievement gain of 

the students. 

The population of students and teachers assumed to be 
covered by this study was all classes in public institutions 
(Grade 1 though undergraduate) where the education is of the 
American (European) type and the students or teachers are 
not selected as being in anyway exceptional. 

Questions Answered in This Dissertation 

The answers to the questions posed in this study were 
as follows: 

1. What is the strength of the relationship between 
teacher clarity and student learning? The correlation 
between teacher clarity (corrected for unreliability in 
measurement) and mean class achievement gain (uncorrected 
for unreliability in measurement) is referred to as the 
effect size in the following. The effect size was 
.35 +/- .05. 



- 95 - 



- 96 - 

2. Do clarity of (a) organization of the lesson (and 
course), (b) explanation (and speech), (c) examples and 
guided practice, and (d) assessment of student learning have 
different relationships to student learning? The effect 
sizes were (a) organization .32 +/- .06; (b) explanation 

.35 +/- .08 (speech .43 +/- .14); (c) examples and guided 
practice .23 +/- .06; and (d) assessment of student learning 
.32 +/- .08. These results overlap each other so they are 
not significantly different. In the regression equations 
the type of teacher clarity is not related to £. 

When two or more dimensions were rated at the same time 
(teacher skill — SKI), the effect size was .60 +/- .13 which 
is significantly higher than the single dimensions of 
teacher clarity (except for clarity of speech). Teacher 
behaviors were only classified as SKI at the college level 
(Grade 13) and the effect size is significantly related to 
grade, so the higher effect size might be due to this 
relationship. 

3. Do student ratings of teacher clarity have a higher 
correlation with student learning than observer ratings? 
The effect size for student ratings was .40 +/- .06 and that 
for observer ratings .28 +/- .05. Student ratings do have a 
higher correlation than observer ratings, but student 
ratings tend to take place in college so the effect might be 
due to this relationship between rater and grade. 

4. Is teacher clarity more important in subjects based 
on student verbal ability or in those based on numerical 



- 97 - 

ability? For both verbal and numerical subjects the effect 
size was .35 +/- .05. 

5. Is teacher clarity more predictive of student 
learning at college, at secondary school, or at elementary 
school? Does the accuracy of prediction vary with grade? 
The effect sizes were (a) elementary school .26 +/- .05; (b) 
secondary school .30 +/- .06? and (c) college .41 +/- .06. 
The effect size for college was significantly higher than 
that for elementary school and was higher, but not 
significantly so, than that for secondary school. The 
correlation between effect size and grade (putting college 
as Grade 13) was about .3, and grade had a significant 
positive regression coefficient when effect size was modeled 
in terms of the variables in this study. The accuracy of 
prediction of teacher clarity does increase with grade 
level. 

6. Is teacher clarity more predictive in large classes? 
With large classes (30 or more students) the effect size was 
.42 +/- .11 and with small classes .28 +/- .05. This is not 
a significant difference. The number of students in the 
class did not produce a significant regression coefficient, 
and the correlation with effect size was only .13. Thus it 
has not been established that teacher clarity is more 
predictive in large classes. 

7. Does teacher clarity have a stronger relationship 
with student learning when the teacher is experienced than 
when she or he is inexperienced? The effect size for 



- 98 - 

experienced teachers was .32 +/- .05 and that for 
inexperienced teachers .31 +/- .07. The effect size was not 
different, but the coded variable for teacher experience did 
have a significant regression coefficient in the model. One 
can conclude that teacher clarity does have a stronger 
relationship with student learning for experienced teachers 
but that the effect is masked by the high negative 
correlation (-.6) between teacher experience and grade (many 
teaching assistants were studied at college level). The low 
values of effect size obtained here (.31 and ,32) compared 
to the overall value of .35 are explained by the fact that 
the seven studies that included (and did not separate) the 
results for both experienced and inexperienced teachers 
produced an effect size of .47 +/- .14. 

8. Which factors present in the investigation of 
relationships between teacher clarity and student learning 
are likely to result in an inaccurate estimation of the 
correlation? The studies that produced effect sizes more 
than two standard deviations from the mean did not 
apparently have anything in common. 

Published studies produced an effect size of 
.37 +/- .05 compared to that for unpublished studies of 
.21 +/- .05. This is a significant difference but 
publication did not result in a significant regression 
coefficient, so a large part of this difference is explained 
by the correlation between publication and both grade and 
student ratings (both of which had correlations with effect 
size of about .3), 



- 99 - 

The studies using a large nximber of classes (40 or 
more) had an effect size of .32 +/- .07 and the others 
.34 +/- .04. The difference is not significant. 

Experimental classes (less than 6 weeks — some only one 
lecture) produced the same effect size as normal classes. 

When only a post test was used and there was no evidence 
of random assignment of students to classes the effect size 
was .60 +/- .09. This was significantly higher than when a 
measure of achievement gain was used. One must conclude 
that posttest-only designs without random assignment are 
unsatisfactory in estimating the correlation between teacher 
clarity and class achievement gain. 

When a simple-gain measure was used the effect size was 
.33 +/- .07 and with residual gain .26 +/~ .05: The 
difference is not significant. The best design is to have 
random assignment of students to classes or to have the same 
students rate different teachers. In this case the effect 
size was .41 +/- .16. The confidence interval is so wide 
because there were only eight effect sizes. 

9. Do the confidence intervals around the mean 
correlations obtained in these various circumstances vary 
significantly with the methods of analysis used? If they 
do, which method is likely to produce the most valid 
interval? If they do not, which is the easiest method? All 
methods gave practically the same results. It is not likely 
to be worth using an elaborate method like Tukey's 
jackknife. The easiest method was that of Glass et al. 
(1981). 



- 100 - 

Discussion 

This study found that the correlation between any 
dimension of teacher clarity of communication and mean class 
achievement gain (effect size) was about .35 +/- .05. This 
indicates that teacher clarity is an important teacher 
characteristic . 

The fact that there were no significant differences 
between the effect sizes of the dimensions of teacher 
clarity raises the issue of whether clarity of organization, 
clarity of explanation, examples and guided practice, and 
assessment of student learning are not separate dimensions 
and that teacher clarity is a unifactor quality. Against 
this view is the fact that when more than one of the 
dimensions were combined in a factor (teaching skill — SKI), 
the effect size increased to about .6. Was this because 
more than one teacher skill was being measured, or was it 
because teacher clarity was being measured more accurately? 
It seems possible that if all four dimensions were combined 
in a factor score, the effect size might be greater than .6. 

Student ratings produce a higher effect size than do 
observer ratings. This is not surprising, as the students 
are in the class all the time and are better able to judge 
what is, or is not, clear to them. 

It is sometimes suggested that teachers of subjects 
based on the students' numerical skills are at a 
disadvantage, compared to teachers of subjects based on the 
students' verbal skills, in obtaining high ratings on 



- 101 - 

student evaluations. This study did not address the 
relative size of the means in the two subject areas, but 
there was no difference in the effect sizes. Thus, teacher 
evaluations, at least for teacher clarity, are equally valid 
in both areas for predicting student learning. 

Teacher clarity is more predictive of student learning 
at college than it is at school. Is it because the standard 
of teaching is more variable at college (the teachers are 
largely untrained), because there is less two-way 
communication at college than at school (often larger 
classes at undergraduate level), because more infomation is 
transferred by the lecture method, or for some other reason? 
This question bears investigation. 

Teacher clarity is a better predictor of student 
learning for experienced teachers than it is for 
inexperienced teachers. Inexperienced teachers might have 
more basic problems (like controlling the class of lack of 
knowledge of the subject) that might be the cause of this. 

Research using experimental classes was successful in 
obtaining the same correlation as in regular classes. Thus, 
investigations often involving the measurement of teacher 
clarity and student learning in a single lecture might be a 
valid means of investigating relationships likely to hold up 
in regular classes. 

Posttest-only designs are unsatisfactory as a measure 
of student learning. There is no evidence that residual- 
gain measures produce a different effect size than 



- 102 - 

simple-gain measures. This is in agreement with the 
arguments of Rogosa and his colleagues (Rogosa, Brandt, & 
Zimowski, 1982; Rogosa & Willett, 1985) and of Zimmerman and 
Williams (1982), that difference scores are reasonably- 
reliable. 

It is not likely to make any difference which of the 
three methods of meta-analysis one uses, so the analyst is 
free to go with her or his preferences. 

One interesting relationship revealed in doing this 
study is that there is a strong correlation between ratings 
of teacher clarity and student learning when the reliability 
of the clarity rating is judged to be high (correlation 
between reliability and effect size is .66). There are only 
10 studies that report both the reliability of the clarity 
measure and at least one effect size, so one is left to 
wonder whether this relationshhip is a stable one and, if 
so, what causes it. Are the studies better in some way when 
the reliability of the teacher clarity measure is high? The 
reliability of clarity measure was significantly related to 
7 of the 10 other variables even though there were only 24 
effect sizes from 10 studies. It would probably be 
productive to investigate some of these relationships. 

This study has been successful in determining the 
confidence interval of the relationship between teacher 
clarity of communication and student learning in the class. 
An effect size of .35 indicates that if the average score of 
classes of similar students on a test is 50%, the students 



- 103 - 

in a class where the teacher is rated high on clarity of 
communication are likely to have a mean score in the region 
of 67% (50 + r/2; Rosenthal & Rubin, 1982), whereas the 
average score of students in a class where the teacher is 
rated low on teacher clarity is likely to be in the region 
of 33% (50 - r/2). Thus, an effect size of .35 indicates an 
important practical relationship. 

The study has also related clarity of communication in 
the classroom to communication theory and has suggested 
possible dimensions of teacher clarity. The study might 
therefore contribute to the theory of teaching. 





APPENDIX A 
STUDIES USED IN THE META-ANALYSIS 




Table A-- 


1. Teachina Behaviors and Assumed Dimensions of 




Teacher Clarity 




ID Study 


Teaching Behaviors 


Dimen- 
sion 


1 Armento 


(1977) Gives concept definition 


EXP 




Gives concept example 


EXP 




Asks for concept definition 


ASL 




Asks for concept example 


ASL 




Asks low-order question 


ASL 




Asks high-order question 


ASL 




Reviews, summarizes 


ORG 



T act-ively listens to S ASL 

T checks S progress regularly ASL 

T asks open-ended question ASL 

T appears to perceive learning 
rate and adjusts teaching 

accordingly EXP 

T seems confident teaching EXP 

S copies T behavior EGP 

T prepares S for lesson by 

reviewing, outlining, etc. ORG 

continued 



2 Berliner & Tikunoff 
(1977) 



- 104 - 



- 105 - 



Table A-1 ---continued 



3 


Brasskamp, Caulley, & 


TEACHER SKILL* 


EXP 




Costin (1979) 


1. Put material across in 








an interesting way 
















curiosity 








3. Explained clearly and to 








the point 








4. Skillful in observing 








student reactions 








5. General (all-round) 








teaching ability 








TEACHER CONTROL* 


ORG 






1 Dp>-Finp>d obiectives of 








discussion 








2. Controlled direction of 
















3. Defined content of 








discussion 








4. Asked specific, drill-type 












4 


Brophy & Evertson 


S show clear understanding 






^ i y / ^ J 




EXP 


5 


Bryson (1974) 


Presents lecture material 








"i n <r» "V V* c o T "X/o 








clear manner 


EXP 






Carefully listens to and 










ASL 






Gives clear and concise 








answers to questions 


EGP 


6 


Doyle & Crichton (1978) 


Clearly presented subject 


EXP 


7 


Doyle & V/hitely (1974) 


Expositional skills 


EXP 


8 


Crocker & Brooker 


Teacher presentation 


EXP 



(1986) 



continued 



- 106 - 



Table A-1 — continued 



ID Study Teaching Behaviors Dimen- 

sion 



9 


Dunkin (1978) 


Number of vague terms by T 


SP 






T structuring (coverage) 


ORG 






T structuring (repetition) 


ORG 






Relevant high-level guest. 


ASL 






Relevant low-level guest. 


ASL 


10 


Centra (1977) 


Course objectives & 








organization 


ORG 




pT'f^v . TiPon?5i^ci, Fit Reattv 


Presentation clarity 


EXP 




( 1975) 






12 


Good & Grouws (1979) 


Conducts review 


ORG 






Siiinma T"i 7P «5 ni'evious dav's 








material 


ORG 






Checks homework 


ASL 






S accountable for seatwork 


ASL 






S accountable for practice 


ASL 






Demonstrations during 








presentation 


EGP 






T— r'ondur't ed «?patwoj*k' fSU) 


EGP 






T actively engaaes S in SV/ 


EGP 






T available for help in SW 


EGP 


13 


Hoffman 


T KNOWLEDGE AND SKILL* 


SKI 






1. Explained how the topics 








were related to each other 








2. Used examples 








3. Knew the subject matter 








T asked S guestions 


ASL 



continued 



- 107 - 

Table A-l--continued 



ID Study Teaching Behaviors Dimen- 

sion 



14 Marsh & Overall (1980) ORGANIZATION* ORG 

1. Course materials and 
objectives were clearly 
outlined 

2. Class presentations 
were well-prepared 

ENTHUSIASM/CONCERN* EXP 

1. T was enthusiastic 

2. T gave presentations that 
made the subject 
understandable 

3. T was concerned that 
S understood 

INTERACTION ASL 
1 . S were encouraged to ask 
meaningful questions, 
to seek help, and to 
express their own ideas 



15 


Page (1958) 


T--chosen comments on tests 








versus no comments 


EGP 


16 


L. Smith (1985) 


Absence of vagueness terms 


SP 






Lesson structure (kinetic) 


EXP 


18 


Solomon, Rosenberg, 


Clarity & Expressiveness vs 






& Bezd'3k ( 1964) 


Obscurity & Vagueness 


EXP 


19 


Sullivan & Skanes (1974) 


OVERALL I^TING* 


EXP 






1. Presents material in a 








clear and easily 








understood manner 








2. Gets Ss really 








interested in the subject 








3. Interest in students 





continued 



- 108 - 



Table A- l--continued 



ID Study Teaching Behaviors Dimen- 

sion 



17 Orpen (1980) 



SKILL* EXP 

1. All-round teaching ability 

2. T ability in observing 
S reactions 

3. Stimulating the 
intellectual curiosity 
of the S 

4. Explaining clearly and 
to the point 

5. Puts material across in 
an interesting way 

STRUCTURE* ORG 

1. Deciding what should be 
done and how 

2. Following an outline 
closely 

3. Concern for keeping a 
tight schedule 

FEEDBACK* EGP 

1. Telling rhe S when they 
have done a particularly 
good job 

2. Complimenting S in front 
of others 

3. Criticizing poor work 

4. Keeping S well-informed 
of their progress 



20 Wright & Nuthall (1970) Terminal structuring 

Recapitulation 
Review 

Structuring total 



21 Gage, Belgard, Dell, 
Hiller, Rosenshine, 
& Unrah (19680 



Presentation clarity 
Pacing the lecture 
Clarity of aims 
Organization of lecture 



ORG 
ORG 
ORG 
ORG 
EXP 
EXP 
ORG 
ORG 



continued 



- 109 - 



Table A-1 — continued 



ID 


Study 


Teaching Behaviors Dimen- 
sion 


22 


Flanders (1970) 


T questions ASL 


23 


Benton (1976) 


CONTENT MEANINGFUL* EXP 

1. T lectures are not over 
my head 

2. T speaks clearly 

3. T makes the connection 
clear between ideas 

4. T explains clearly 






PLANNING & LEARNING CLIMATE* ORG 

1. T used classtime well 

2. T accomplished objectives 

3. Objectives for the 
course were made clear 

4. T summarized or 
emphasized major points. 


24 


Morsh, Burgess, 
S. Smith (1956) 


(Reliability measure only) 


25 


Peterson, Micceri, 
& Smith (1985) 


(Reliability measure only) 


26 


Poonyakanok, Thisayakorn 
& bigby (1986) 


, (Reliability measure only) 


27 


Bourke (1985) 


Extent of coverage of 

posttest items ORG 

Review to refresh learning ORG 

Homework used EGP 

T gave help during lessons EGP 

Total number of T questions ASL 


29 


Costin (1978) 


T SKILL* SKI 

1. T put material across in 
an interesting way 

2. T stimulated the 



intellectual curiosity 
of the S 

3. T was skillful in 
observing S reactions 

4. Overall rating of T 



continued 







- 110 






Table 


A- 1" -continued 




X JJ 




Teaching Behaviors Dimen- 






sion 


^ o 




(Reliability measure only) 






& Menges ( 1971) 








K+- con ^ RynnViv ^ 1 Q 74 ^ 


o snow cxear unaei suanamy 








of T presentations 


EXP 






T goes to seats to check 








work 


ASL 






T uses advance organizers 








uo xntrouuce acuxvxuxes 








T well— organized , prepared 








T goes to Ss desk to give 








help 


EGP 


31 


Hiller, Fisher, & Kaess 


Lack of vagueness 


SP 




( 1969) 






32 


nines, Cruickshank, & 


TEACHER CLARITY* 


SKI 




Kennedy (1985) 


1. T stresses important 








aspects of content 








2. T explains content by 








use or exampxes 








3. T assesses and responds 








to perceived deficiencies 








in understanding 




•J o 




T asks low-level question 


ASL 




Dufour (1986) 






34 


T.nrpnt 7 f 1 977 ) 


Ability to communicate 








effectively with S 


EXP 






Pauses, elicits, and 








responds to S questions 


EGP 






Utilizes S feedback to 








modify teaching 


ASL 


35 


McDonald & Elias (1976) 


Directed S seatwork 


EGP 


36 


Marsh (1987) 


(Reliability measures only) 





continued 



Table A-1 — continued 



- Ill - 



ID 


Study 




Teaching Behaviors Dimen- 
sion 


37 


Pinney (1970) 




T announcements about 

important points EXP 

Vocal intensity: significant 
variation in pitch 
and/or volume SP 


38 


Shave 1 son & 

Dempsey-Atvood 


( 1976) 


(Reliability measures only) 


39 


Austin (1976) 




ComiTients on homework versus 

just grading EGP 


40 


McKeachie, Linn, 
(1971) 


& Mann 


SKILL* EXP 

1. All-round teaching ability 

2. T ability in observing S 
reactions 



3. Stimulating the intellectual 
curiosity of the S 

4. Explaining clearly and 
to the point 



STRUCTURE* ORG 

1. T decided in detail what 
should be done and how 
and how it should be done 

2. T followed an outline closely 

3. T had everything going 
according to schedule 

FEEDBACK* EGP 

1. T told S when they had 
done a particularly good 
job 

2. T complimented S in 
front of others 

3. T criticized poor work 



41 Marsh, Fleiner, Class presentation EXP 

& Thomas (1975) 



continued 



Table A-1 — continued 



- 112 - 



ID Study Teaching Behaviors Dimen- 

sion 



42 Hazelton (1980) FACILITATION OF LEARNING* EXP 

1 . S could understand class 
presentations 

2. T delivered orderly, 
logical presentations of 
material 

3. T spoke with poise 

4. T gave organized answers 
to complicated questions 
in class 



43 Gessner (1973) Content and organization ORG 

Presentation EXP 



44 Frey (1973) Planning and organization ORG 

Presentation EXP 







Class discussion 


ASL 


46 


Ellis & Rickard (1977) 


(Reliability measures 


only) 


47 


Foy (1969) 


(Reliability measures 


only) 



Note . T = teacher, S = student. 

* A factor defined by the numbered behaviors 



- 113 - 



Table A- 2. Characteristics and Results of Studies 



ID 


VER 


PUB STU 


ACH 


REL 


TEX 


WKS 


GRA 


No 


NU 


JJlrl 


K 






1 


1 




2 


. 85 




. 4 


c: 
D 


1 A 


z z 


EAir 








1 


1 


1 -i 


2 


.85 




.4 


5 


14 


22 


ASL 


.06 






1 


1 


1 -1 


2 


.85 


-1 


.4 


5 


14 


22 


ORG 


. 38 


.30 


.31 


2 


-1 


1 -1 


• 


• 




3U 


2. 


o c\ 
i\j 


zU 


A CT 

AoLi 




• 


• 


2 


-1 




• 


• 




JU 


2. 


JU 


zU 




A 
. ^D 






2 


-1 


1 -1 








30 


2 


30 


20 


EGP 


.32 






2 


-1 


1 -1 


• 


• 


1 


30 


2 


30 


20 


ORG 


.32 


• 


• 


2 


1 


1 -1 


• 


• 




JU 


2. 


JU 


zU 


AbLi 


AOi 


• 


• 


2 


1 




• 


• 




30 


2. 


30 


zU 


EaF 


. D J 


* 




2 


1 


1 -1 








30 


2 


30 


20 


EGP 


.27 






2 


1 


1 -1 


• 






30 


2 


30 


20 


ORG 


.50 


• 


• 


2 


-1 


1 -1 


• 


• 




30 


5 


30 


20 


ASL 


. 34 


• 


• 


2 


-1 




• 


• 




30 


5 


30 


20 


EXP 


. 3z 


• 


* 


2 


-1 


1 "l 








30 


5 


30 


20 


EGP 


.06 






2 


-1 


1 -1 


• 




1 


30 


5 


30 


20 


ORG 


.42 


• 


• 


2 


1 


1 -1 








30 


5 


30 


20 


ASL 


.33 






2 


1 


1 -1 


• 


• 


1 


30 


5 


30 


20 


EXP 


.44 


• 


• 


2 


1 




• 


• 




30 


5 


30 


zU 


EGP 


,11 






2 


1 


1 -1 






} 


30 


5 


30 


20 


ORG 


.37 


.38 


.40 


3 


1 




1 


. 85 




8 


13 


ZD 


i / 


EXP 


. JO 






3 


1 




1 


o c 




o 
8 


i J 


ZD 


1 "7 
1 / 


ORG 


. J J 


. JZ 


. J J 


4 


-1 




3 


• 




30 


2.5 


30 


30 


EXP 


.07 






4 


1 




3 




} 


30 


2.5 


30 


30 


EXP 


.15 


.11 


.11 


5 


-1 




• 






30 


2 


30 


20 


EXP 


.37 






5 


-1 




• 


• 


1 


30 


2 


30 


20 


ASL 


.39 






5 


-1 




• 


• 




i(J 






zU 


EGP 


• J4 


• J / 


• 41 


5 


1 




— > 

3 


. 78 




15 


13 


21 


1 2 


EXP 


. 20 


. 20 


. 20 


7 


1 


1 1 


3 


• 


-1 


15 


13 


15 


12 


EXP 


.31 


.31 


.32 


8 


-1 




2 


• 




30 


2 


30 


36 


EXP 


. 15 


• 


• 


8 


1 


i -1 


2 




J 


30 


2 


30 


36 


EXP 


.36 






8 


-1 


1 -1 


2 




1 


30 


5 


30 


36 


EXP 


-.18 


• 


• 


8 


1 


1 -1 


2 


• 




30 


5 


30 


36 


EXP 


. 15 


. 12 


.12 


9 


1 




3 






. 2 


6 


35 


29 


SP 


.08 






9 


1 




3 






.2 


6 


35 


29 


ORG 


.30 






9 


1 




3 






.2 


6 


35 


29 


ASL 


.10 


.16 


.16 


10 


-1 




1 






15 


13 


« 


30 


ORG 


.30 






10 


1 




1 






15 


13 




30 


ORG 


.48 


.39 


!41 



continued 



- 114 - 



Table A-2 — continued 



ID 


VER 


PUB 


STU 


ACH 


REL 


TEX 


WKS 


GRA 


NS 


NC 


DIM 


R 


SMR 


SMZ 


1 1 


_^ 


1 




2 




_^ 


15 


13 


26 


17 


EXP 


.42 






11 


-1 


1 


1 


2 




-1 


15 


13 


26 


17 


ASL 


.21 






11 


1 


1 


1 


2 


• 


-1 


15 


13 


26 


17 


EXP 


. 81 


• 


• 


11 


1 


1 


1 


2 




-1 


15 


13 


26 


17 


ASL 


.40 


.46 


.50 


12 


-1 


1 


_^ 


3 






30 


4 


25 


40 


ORG 


. 29 






12 


-1 


1 


-1 


3 




1 


30 


4 


25 


40 


ASL 


.34 




. 


12 


-1 


1 


-1 


3 


• 


1 


30 


4 


25 


40 


EGP 


. 18 


. 27 


. 28 


13 




1 




1 


.82 


0 


15 


13 


30 


142 


SKI 


.56 






13 




1 




1 


.82 


0 


15 


13 


30 


142 


ASL 


.59 


• 


• 


13 


J 


1 




3 


. 82 


0 


15 


13 


30 


142 


SKI 


. 29 






13 


1 


1 


1 


3 


.82 


0 


15 


13 


30 


142 


ASL 


.36 


.45 


.49 


14 


_^ 


1 




2 




-1 


15 


13 


31 


31 


ORG 


. 22 






14 


-1 


1 


1 


2 




-1 


15 


13 


31 


31 


EXP 


.40 


. 




14 


-1 


1 


1 


2 




-1 


15 


13 


31 


31 


ASL 


.36 


.33 


.34 


15 




1 


_^ 


3 




1 


3 


9 


9 


149 


EGP 


. 14 


.14 


. 14 


16 




1 




3 


. 91 


1 


. 1 


10 


25 


19 


SP 


.47 






16 


-1 


1 


-1 


3 


.91 


1 


.1 


10 


25 


19 


EXP 


.51 


.49 


.54 


17 


-i 


1 


1 


3 


• 


-1 


15 


13 


10 


10 


ORG 


.10 


• 


• 


17 




1 




3 


• 


-1 


15 


13 


10 


10 


SKI 


.61 






17 


-1 


1 


\ 


3 




-1 


15 


13 


10 


10 


EGP 


. 21 


. 31 


. 32 


18 




1 




1 




-1 


15 


13 


25 


24 


EXP 


. 58 


. 58 


. 66 


19 


~\ 


1 


\ 


4 


• 


0 


15 


13 


25 


70 


EXP 


.41 


• 


• 


19 




1 




• 4 




0 


15 


13 


25 


70 


EXP 


. 21 






19 


-1 


1 


1 


4 




1 


15 


13 


25 


70 


EXP 


.53 






19 


-1 


1 


1 


4 




-1 


15 


13 


25 


70 


EXP 


.01 


.29 


.30 


20 


-1 


1 


-1 


3 




0 


.6 


3 


25 


17 


ORG 


.07 


.07 


.07 


21 




-1 




4 




1 


. 1 


12 


21 


43 


FXP 


26 






21 


1 


-1 


1 


4 




1 


.1 


12 


21 


43 


ORG 


.32 


.29 


.29 


22 




1 








1 

X 




•? 


tit -J 


1 S 


r\0 -Li 








22 


1 


1 


-1 


3 




1 


2 


4 


25 


16 


ASL 


-.19 






22 




1 




3 




1 


12 


6 


25 


30 


ASL 


.11 






22 




1 




3 




1 


2 


7 


25 


15 


ASL 


-.06 






22 




1 




3 




. 1 


2 


8 


25 


16 


ASL 


.44 


.10 


.10 


23 




-1 




0 




-1 


10 


13 


17 


31 


EXP 


-.19 






23 




-1 




0 




-1 


10 


13 


17 


31 


ORG 


.11 


-.04 


-.04 


24 


-1 


1 


1 


3 


.42 


1 


2 


12 


14 


102 


• 


• 


• 


• 



continued 



- 115 - 



Table A-2--continued 



ID 


VER 


PUB 


STU 


ACH 


REL 


TEX 


WKS 


GRA 


NS 


NC 


DIM 


R 


SMR 


SMZ 


25 


• 


• 


• 


• 


.86 


• 


• 


• 


• 


• 


• 


• 


• 


• 


26 


• 


• 


• 


• 


.71 


• 


• 


• 


• 


• 


• 


• 


• 


• 


27 


-1 




-1 


• 


• 


1 


12 


10 


25 


75 


ORG 


. 30 






27 


-1 


1 


-1 


• 


• 


1 


12 


10 


25 


75 


EGP 


.24 






27 


-1 


1 


-1 


• 


• 


1 


12 


10 


25 


75 


ASL 


.00 


.18 


.18 


28 


• 




• 


• 


Q O 


• 


• 


• 


• 


• 


• 


• 






29 


1 


1 


1 


4 


• 


-1 


16 


13 


14 


96 


SKI 


.49 


.49 


.54 


30 


1 


-1 


-1 


3 


.75 


1 


30 


2.5 


25 


75 


EXP 


.20 


• 


• 


30 


1 




-1 


3 


. 75 


1 


3U 


1 . 5 


ZD 


/D 


AbL 


. Uz 






30 


1 


-I 


-1 


3 


.75 


1 


30 


2.5 


25 


75 


ORG 


.27 


• 


• 


30 


1 




-1 


J 


. /d 


1 
1 


JU 


Z . D 


ZD 


1 D 




1 Q 


* 


* 


30 


-1 


-1 


-1 


3 


.75 


1 


30 


2.5 


25 


75 


EXP 


.17 


• 


• 


30 


- 1 




-1 


3 


. 75 


1 


3U 


i. ,^ 


ZD 


/ D 


AbL 








30 


-1 


-\ 


-1 


3 


.75 


1 


30 


2.5 


25 


75 


ORG 


.04 






30 


- 1 




— 1 


J 




1 


■5U 


/ . 3 


ZD 


/ D 




1 Q 


. 1 z 


1 o 
• 1 z 


31 


1 


1 


-1 


3 


• 


1 


.1 


12 


21 


55 


SP 


.54 


.54 


.60 


32 


-1 


1 


-1 


1 


. 86 


-1 


. 2 


13 


5 


32 


SKI 


.68 






32 


-1 


1 


1 


1 


.87 


-1 


.2 


13 


5 


32 


SKI 


.61 


.65 


.78 


33 


-1 


1 


-1 


3 


• 


1 


12 


7.5 


30 


29 


ASL 


.11 


.11 


.11 


34 


0 


-1 


-1 


3 


.45 


1 


20 


5.5 


30 


26 


EXP 


-.03 






34 


(J 


-1 


- 1 


3 


.4b 


i 


ZD 


D . D 




O £^ 
ZD 


A OT 

AbL 






1 Q 

— . i y 


35 


1 


-1 


-1 


2.5 


.45 


1 


30 


2 


20 


41 


EGP 


.4 


• 


• 


35 


1 




-1 


2 . 5 


. 45 


1 


30 


5 


20 


41 


EGP 


. 4 






35 


-1 


-I 


-1 


2.5 


.45 


1 


30 


2 


20 


41 


EGP 


.1 






35 


-1 




-1 


2 . 5 


.45 


1 


30 


5 


20 


41 


EGP 


- . 2 


.18 


. 1 8 


36 


0 




1 


• 


. 77 


• 


15 


13 


100 


• 


• 


• 


• 


• 


37 


1 




-1 


3 


• 


-1 


.2 


8.5 


25 


32 


EXP 


.35 






37 


1 




-1 


3 


• 


-1 


.2 


8.5 


25 


32 


SP 


. 35 


. 35 


. 37 


38 


0 




-1 


• 


.63 


• 


• 


• 


• 


• 


• 


• 






39 


-1 




-1 


3 


• 


1 


6 


7 


12 


18 


EGP 


.47 


.47 


.51 


40 


1 




1 


3 


• 


-1 


15 


13 


25 


143 


EXP 


. 14 






40 


1 




1 


3 


• 


-1 


15 


13 


25 


143 


ORG 


.01 






40 


1 




1 


3 


• 


-1 


15 


13 


25 


143 


EGP 


.14 


.10 


.10 



continued 



Table A- 2 — continued 



- 116 - 



ID VER PUB STU ACH REL TEX WKS GRA NS NC DIM R SMR SMZ 



41 


-1 1 


1 3 


-1 


15 


13 


40 


18 


EXP 


.43 


.43 


.46 


42 


-1 -1 


1 3 


.89 -1 


10 


13 


30 


36 


EXP 


-.25 - 


.25 


.26 


43 


-1 1 


1 4 




15 


13 


78 


20 


ORG 


.77 






43 


-1 1 


1 4 




15 


13 


78 


20 


EXP 


.69 


!73 


.93 


44 


-1 1 


1 3 




10 


13 


35 


13 


ORG 


.67 






44 


-1 1 


1 3 




10 


13 


35 


13 


EXP 


.79 


!73 


!93 


45 


-1 1 


1 3 




15 


13 


35 


53 


ORG 


.68 






45 


-1 1 


1 3 




15 


13 


35 


53 


EXP 


.62 






45 


-1 1 


1 3 




15 


13 


35 


53 


ASL 


.37 


'.56 


.63 



46 
47 



87 
71 



Note . ID: identification number of study; VER: 1 = subject 
based on students' verbal ability, -1 = subject based on 
students' numerical ability, 0 = both subject areas; PUB: 
1 = study published in a journal or book, -1 = dissertation or 
ERIC; STU: 1 = student rating of teacher clarity, 
-1 = observer rating of teacher clarity, 0 = rating by both 
students and observers; ACH: 0 = essay test rated by the class 
teacher, 1 = posttest only with no random assignment of 
students, 2 = simple gain score, 3 = residual gain score, 
4 = random assignment of students or the same students rate 
all the teachers; REL = reliability of the teacher-clarity 
measure; TEX: 1 = experienced teachers, -1 = student teachers 
or teaching assistants; WKS = weeks between the start of the 
course and the posttest; GRA = grade (college = 13, 
8.5 = grades 8 and 9); NS = average number of students in a 
class; NC = number of classes in the study; DIM: ORG = clarity 
of organization, EXP = clarity of explanation, EGP = examples 
and guided practice, ASL = assessment of student learning, 
SP = clarity of speech, SKI = a factor score comprising more 
than one dimension and no one dimension is dominating the 
factor; _R = the correlation between the dimension of teacher 
clarity and the achievement gain of the class (the average 
value of all the rs reported in the study for the dimension, 
the grade, and the subject area); SMR = the study mean value 
of R_ averaged over all the ^s for the study; SMZ, = the Fisher 
z equivalent of SMR. 



APPENDIX B 
REJECTED STUDIES 



Table B-1. Rejected Studies and Reason for Re lection 



Study 


Reason for Rejection 


Aagard (1973) 


No class-level rs 


Abrami & Mizener (1982) 


Grades used as achievement measure 


Amidon & Giammatteo 
[ 1 yo / ) 


Superior T nominated by 
administrators 


Aubrect (1981) 


Review 


Baird (1983) 


No data 


Bendig (1953) 


Overall teacher rating only 


Bennett & Jordan (1976) 


No class-level rs 


Benton (1982) 


Review 


Beseda (1973) 


No class-level jcs 


Blaney (1983) 


No class- level rs 


Brown ( 1977 ; 


No TC variables 


Bush, Kennedy, 

& Cruickshank (1977) 


No achievement measures 


Chase & Keene (1979) 


No class-level _rs 


Clark, Gage, Marx, 
Peterson, Stayrook, 
& Winne (1979) 


No class-level rs 


Cobb (1972) 


No T behaviors observed 


Cook (1967) 


No TC variables 


Cooley & Leinhardt 
(1980) 


No TC variables 




continued 




- 117 - 



- 118 - 



Table B-1 — continued 



Study- 



Reason for Rejection 
No TC variables 



Crawford, Evert son, 
Anderson, & Brophy 
( 1976) 



Creamer & Lorentz (1979) No class-level rs 

Cruickshank (1985) Applications of TC only 

Domino (1971) No TC variables 

Doyle (1979) No TC variables 

Druva & Anderson (1983) T Characteristics not behaviors 

Duffy, Roehler, Meloth, Qualitative research 
& Vaurus (1986) 

Ellis & Rickard (1977) Overall rating of T only 

Endo & Delia-Piano (1976)No class-level rs 



Evans & Guyman (1978) 

Evert son, Anderson, 
Anderson, & 
Brophy (1980) 

Evertson, Anderson, 
& Brophy (1979) 

Feldman (1976) 

Tollman (1983) 

Good & Brophy (1974) 

Good & Grouws (1977) 

Goodlad & Klein (1974) 

Green (1983) 

Hammer (1972) 

Heil, Powell, & 
Felfer (1960) 

Hsu & White (1978) 



No class-level rs 
No class-level iS 

No class-level rs 

Review 
Review 

No TC variables 
No class- level rs 
No class-level r.s 
No TC variables 
No class-level _rs 
No TC variables 



Canonical correlations reported 
between achievement and unknown 
factors of student assessment 



continued 



Table B-1 — continued 



- 119 - 



Study 

Land (1979) 

Land (1980) 

Land (1981a) 

Land (1981b) 

Land & Denham (1979) 

Lorentz & Coker (1979) 

Marsh (1977) 

Marsh (1982) 

Martikean (1973) 

Mathis & Shrxiin (1977) 

Medley & Mitzell (1959) 

McKeachie & Kulik, 
( 1975) 

McKeachie & Linn (1978) 

McKeachie, Linn, & 
Mendelson (1978) 

McKeachie & Solomon 
(1958) 

McKinney, Mason, 

Parkinson, & Clifford 
(1975) 

Morsh, Burgess, & 
Smith (1956) 

Peterson, Micceri, 
& Smith (1985) 

Peterson (1979) 

Peterson, Marx, 
& Clark ( 1978) 

Pitman (1985) 



Reason for Rejection 
No class- level ^s 
No class-level rs 
No class-level rs 
No class- level rs 
No class-level j^s 
No class- level rs 
No class-level rs 
No achievement measure 
No class- level rs 
No class- level rs 
No achievement measures 
Review article 

No TC variables, no achievement 
Overall rating of T only 

No achievement measure 

Only S behavior reported 

Overall rating of T only 

No TC variables 

No TC variables 
No TC variables 

No achievement measures 



continued 



- 120 



Table B-1 — continued 



Study- 
Rodin & Rodin (1972) 
Rosenshine (1970a) 
Rosenshine (1970b) 
Ryan (1973) 

Ryan (1974) 

Savage (1972) 

Sharp (1966) 

L. Smith (1979) 

L. Smith (1985b) 

L. Smith (1985c) 

Smith & Cotton (1980) 

Smith & Sanders (1981) 

A. Snider (1965) 

R. Snider (1966) 

Soar (1968) 

Soar (1972) 

Soar (1973) 

Soar & Soar (1973) 

Solomon & Kendall (1976) 

Stallings (1974) 

Stallings (1977) 

Stallings & Kaskowitz 
( 1974) 

Tobin & Capie (1982) 

Torrance & Parent (1966) 



Reason for Rejection 
Overall rating of"T only 
Review 
Review 

No class- level rs. Controls 
were not taught relevant content 

No class-level rs 

No class-level rs 

No TC variables 

No class-level rs 

No class-level rs 

No class- level rs 

No class- level rs 

No class-level _rs 

No TC variables 

No class-level rs 

No TC variables 

Review 

No TC variables 
No TC variables 
No TC variables 
No TC variables 
No TC variables 
No TC variables 

No class- level ^s 
No class-level rs 



continued 



- 121 - 

Table B-1 — continued 

Study Reason for Rejection 

Trinchero (1974) No TC variables 

Trinchero (1975) No TC variables 

Trindade (1972) No class-level r^s 

Turner & Thompson (1973) The first exam after 3 weeks 

(which correlated .73 with TC) 
was used as the pretest. 
Thus TC is largely partialed 
out of the rs. 

Vorrayer (1969) No TC variables 

Zelby (1974) No class-level j;s 

Note. T = teacher, S = student, TC = teacher clarity. 



REFERENCES 



Aagard, S. A. (1973). Oral questioning by the teacher: 
Influence on student achievement in eleventh grade 
chemistry. Dissertation Abstracts International , 34, 
631A. (University Microfilms No. 73-19406) 

Abrami, P. C. , & Mizener, D. A. (1982, August). 

Student/instructor attitude similarity, course ratings , 
and student achievement . Paper presented at the Meeting 
of the American Psychological Association, Washington, 
DC. (ERIC Document Reproduction Service No. ED 223 144) 

Amidon, E. J., & Giammatteo, M. (1967). The verbal 

behavior of superior elementary school teachers. In E. 
J. Amidon & J. 13. Hough (Eds.), Interaction analysis; 
Theory, research and application (pp. 186-187). 
Reading, MA: Addison- V/esley. 

Armento, B. (1977). Teacher behaviors related to student 
achievement on a social science concept test. Journal 
of Teacher Education , 28(2), 46-52. 

Aubrect, J. D. (1981). Reliability, validity, and 

generizability of student ratings of instruction (IDEA 
Paper No. 5T^ Kansas State University: Center for 
Faculty Evaluations and Development. (ERIC Document 
Reproduction Service No. ED 213 296) 

Austin, J. D. (1976). Do comments on mathematics homework 
affect student achievement? School Science and 
Mathematics , 76, 159-164. 

Baird, J. S. (1983). Validity and reliability of student 
ratings of faculty. Teaching of Psychology , 10(1), 46. 

Bendig, A. W. (1953). The relation of level of course 

achievement to students' instructor and course rating in 
introductory psychology. Educational and Psychological 
Measurement , 13, 437-448. 

Bennett, N., & Jordan, J. (Eds.) (1976). Teaching styles 
and pupil progress . Cambridge, MA: Harvard University 
Press. 



- 122 - 



- 123 - 



Benton, S. E. (1976, April). A comparison of the criterion 
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BIOGRAPHICAL SKETCH 



I, Frank Fendick, was born in 1930 in married quarters 
in the "Home of the British Army," Aldershot, England. I 
attended army schools until I joined the army as a boy 
soldier in 1945 at the age of 15 (just in time to serve for 
six months during the war and thus qualify for the V/ar 
medal). I served in the army for 11 years, mostly as a 
radio mechanic in the Airborne. I served in Egypt and 
Cyprus but was discharged, with the rank of sergeant, in 
February, 1956, so it was not necessary for me to go to jail 
for refusing to take part in Britain's attack on Egypt in 
that year. 

I attended an agricultural college for a year to obtain 
my Certificate in Agriculture and worked as a cowman until, 
with a wife and child to support, I decided that six pounds 
(perhaps $18 then) a week did not provide a comfortable 
living. In 1959 I started at a technical college 
(eguivalent to community college) and in 1961 obtained my 
A-levels (university entrance qualification) in math and 
physics. I was then employed at the college to teach these 
subjects at 0-level (two years below A-level) at the 
princely salary of 700 pounds per year (thus doubling my 
previous wage). 



- 137 - 



- 138 - 

After three years teaching I entered Queen Mary 
College, London University, where I obtained a B.Sc. 
(Honours) in physics. I then taught A-level physics at my 
previous college for eight years. During this period I 
obtained, by part-time study, two certificates in education 
and a Diploma in Education from Leeds University. In 
partial fulfillment of this gualif ication I wrote a thesis 
entitled "The Effects of Teacher-Student Classroom 
Interaction on Student Achievement and Student Opinion of 
the Teacher. " 

In 1975 my wife left me, so I went to Africa. Instead 
of joining the Foreign Legion, I taught physics at the 
University of Maiduguri and at the Federal Advanced 
Teachers' College, Yola (both in Nigeria), where I was the 
head of department. By this time I thought I knew guite a 
lot about teaching and was not impressed by the research 
that I read on the subject. I, therefore, decided to do 
some research of my own using the scientific principles that 
I taught in physics. In 1982 I became a graduate student in 
the Foundations of Education Department, University of 
Florida, in order to accomplish this goal. While at the 
university I have taught half-time in the Physics Department 
and have been paid $15,000 a year--more than I have ever 
earned in my life! 



I certify that I have read this study and that in my 
opinion it conforms to acceptable standards of scholarly 
presentation and is fully adequate, in scope and quality, as 
a dissertation for the degree of Doctor of Philosophy. 

--'^^'^^y::±^-^^::d^^ 

James Algina, Chaitrhan 

Professor of Foundiatiions of Education 



I certify that I have read this study and that in my 
opinion it conforms to acceptable standards of scholarly 
presentation and is fully adequate, in scope and quality, as 
a dissertation for the degree of Doctor of Philosophy. 




V/ilson H. Guertin 

Professor of Foundations of Education 



I certify that I have read this study and that in my 
opinion it conforms to acceptable standards of scholarly 
presentation and is fully adequate, in scope and quality, as 
a dissertation for the degree of Doctor of Philosophy. 



Patricia T. Ashton 

Professor of Foundations of Education 



I certify that I have read this study and that in my 
opinion it conforms to acceptable standards of scholarly 
presentation and is fully adequate, in scope and cmality, as 
a dissertation for the degree of Doctor of Phil<y^dphY . 



Robert C. Zi 
Professor of Psychology 




This dissertation was submitted to the Graduate Faculty of 
the College of Education and to the Graduate School and was 
accepted as partial fulfillment of the requirements for the 
degree of Doctor of Philosophy. 



August, 1990 




Chairman, Foundatio; 



^V:^ 

of Education 



Dean, College of Education 



Dean, Graduate School